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This document is a preface to a special issue on computational linguistics and natural language processing. It highlights the increasing presence of intelligent robots in various aspects of our lives, such as customer service, manufacturing, and security. The document emphasizes the importance of robots being able to communicate using human language, which requires the study of computational linguistics and natural language processing. The special issue includes papers on topics such as linguistic profiling, higher-order logical representations, deciphering scripts, and distinguishing between human-written and machine-generated texts. The document concludes by expressing gratitude to the reviewers and contributors of the special issue. [Extracted from the article]

ThisSpecial Issue presents the latest advances in research and novel applications of speech and language technologies based on the works presented at the sixth edition of the IberSPEECH conference held in Granada in 2022, paying special attention to those focused on Iberian languages. IberSPEECH is the international conference of the Special Interest Group on Iberian Languages (SIG-IL) of the International Speech Communication Association (ISCA) and the Spanish Thematic Network on Speech Technologies (Red Temática en Tecnologías del Habla, or RTTH for short). Several researchers were invited to extend the contributions presented at IberSPEECH2022 due to their interest and quality. As a result, the Special Issue is composed of 11 papers that cover different research topics related to speech perception, speech analysis and enhancement, speaker verification and identification, speech production and synthesis, natural language processing, together with several applications and evaluation challenges. [ABSTRACT FROM AUTHOR]

The papers in this special issue provide a critical look at some historical ideas that have had an influence on research and teaching in the field of speech communication. They also address widely used methodologies or address long-standing methodological challenges in the areas of speech perception and speech production. The goal is to reconsider and evaluate the need for caution or replacement of historical ideas with more modern results and methods. The contributions provide respectful historical context to the classic ideas, as well as new original research or discussion that clarifies the limitations of the original ideas. [ABSTRACT FROM AUTHOR]

Code-switching (CS) in spoken language is where the speech has two or more languages within an utterance. It is an unsolved issue in automatic speech recognition (ASR) research as ASR needs to recognise speech in bilingual and multilingual settings, where the accuracy of ASR systems declines with CS due to pronunciation variation. There are very few reviews carried out on CS, with none conducted on bilingual and multilingual CS ASR systems. This study investigates the importance of CS in bilingual and multilingual speech recognition systems. To meet the objective of this study, two research questions were formulated, which cover both the current issues and the direction of the research. Our review focuses on databases, acoustic and language modelling, and evaluation metrics. Using selected keywords, this research has identified 274 papers and selected 42 experimental papers for review, of which 24 (representing 57%) have discussed CS, while the rest look at multilingual ASR research. The selected papers cover many well-resourced and under-resourced languages, and novel techniques to manage CS in ASR systems, which are mapping, combining and merging the phone sets of the languages experimented with in the research. Our review also examines the performance of those methods. This review found a significant variation in the performance of CS in terms of word error rates, indicating an inconsistency in the ability of ASRs to handle CS. In the conclusion, we suggest several future directions that address the issues identified in this review. [ABSTRACT FROM AUTHOR]

Non-speaking autistic individuals grapple with the profound impact of the 'double empathy problem' in their daily interactions with speaking others. This study rethinks the communication challenges faced by non-speaking autistic individuals, challenging traditional approaches that predominantly focus on speech and complex communication devices (AAC). By spotlighting the natural phenomenon of "interactive stimming," a powerful mode of communication among autistic individuals, this study advocates for a shift from a conventional emphasis on speech towards the foundational role of the body in autistic communication. Central to this exploration is the introduction of the Magical Musical Mat (MMM), an innovative interactive environment mapping interpersonal touch to musical sounds. Through a robust mixed-methods approach integrating video-based fieldwork and designbased research, this paper engages three non-speaking autistic children and their mothers in a 5-day empirical intervention. Results reveal significant transformations in parent-child interactions as both parents and children are acquainted with touch in a new environment. Children assert their autonomy, exploring at their own pace, and discovering sensory features of the environment. Notably, the introduction of sound prompts heightened awareness of the stims, leading to diversified and expressive stim movements. Foregrounding interpersonal touch eventually guides parents into their children's sensory activities where parents attune to the stims of their children by joining in and facilitating their expressiveness, co-creating extended, evolving patterns of repetitive cycles. The collaborative stim cycles can be likened to free improvisation, where dynamical coherence between individuals occurs through a blend of stability and active flexibility. By shifting the focus from speech to co-created sensory experiences, this paper sheds light on the value of transformative multisensory environments, envisioning a world where varied modes of communication are valued and embraced. [ABSTRACT FROM AUTHOR]

This study is dedicated to developing an innovative method for evaluating spoken English by integrating large language models (LLMs) with effective space learning, focusing on the analysis and evaluation of emotional features in spoken language. Addressing the limitation of current spoken English evaluation software that primarily focuses on acoustic features of speech (such as pronunciation, frequency, and prosody) while neglecting emotional expression, this paper proposes a method capable of deeply recognizing and evaluating emotional features in speech. The core of the method comprises three main parts: (1) the creation of a comprehensive spoken English emotion evaluation dataset combining emotionally rich speech data synthesized using LLMs with the IEMOCAP dataset and student spoken audio; (2) an emotion feature encoding network based on transformer architecture, dedicated to extracting effective spatial features from audio; (3) an emotion evaluation network for the spoken English language that accurately identifies emotions expressed by Chinese students by analyzing different audio characteristics. By decoupling emotional features from other sound characteristics in spoken English, this study achieves automated emotional evaluation. This method not only provides Chinese students with the opportunity to improve their ability to express emotions in spoken English but also opens new research directions in the fields of spoken English teaching and emotional expression evaluation. [ABSTRACT FROM AUTHOR]

BLC Theory proposes that individual differences (IDs) in language proficiency (in both native and non-native speakers) can poorly be mapped on a single proficiency scale. Instead, IDs can best be understood and studied in terms of two fundamentally different dimensions: (1) the cognition of oral language (receptive and productive speech processing) and (2) the cognition of the written language (reading and writing). This paper presents an update of BLC Theory placed under a non-nativist, usage-based, neural-network metatheory of language as a complex system. The paper includes predictions for the absence or presence of IDs in the oral and written domains, separately for native and non-native speakers. The theory predicts that while cognitive factors such as executive functions, non-verbal memory, and intelligence positively affect the acquisition of reading and writing skills in both native and non-native speakers, they do not play a significant role in the acquisition of speech processing in either native or non-native speakers. Contrary to folk wisdom, one does not need to be particularly intelligent to learn to understand and produce speech in a non-native language. Attention is given to typological differences between children's home language(s) and the standard language(s) of literacy. [ABSTRACT FROM AUTHOR]

This paper aims to present observations on the pragmaticalized use as well as the concomitant functions of the demonstrative este in Mexican Spanish. Este seems to serve an additional range of functions in Mexican Spanish in particular, but also in other Hispanic American varieties, as it has turned into a quite productive discourse marker that can be considered, among other things, as a hesitation element, an outline signal, and probably also as an evidentiality marker. The specific functional domains (especially with regard to possible evidential functions) will be illustrated in more detail in the paper using oral language data from the speaker community of the southern Mexican city of Oaxaca de Juárez. The study also includes a sociolinguistically oriented quantitative analysis, the results of which will also be discussed in the context of language ideological attitudes towards este. [ABSTRACT FROM AUTHOR]

Vessel Traffic Service (VTS) significantly improves the navigation efficiency of ports. This paper proposes a model called Joint Extraction of Triples from the VHF Speech (JER‐VHF) to ensure the efficiency of the VTS. Numerous texts are extracted from the Very High Frequency (VHF) speech communication contents and these texts are organized into a dataset named VHFDT. The proposed model's transforming task transforms the voice communication contents of this dataset into a triple representation. VHFDT has a large number of overlapping triples. Therefore, this paper proposes a combined model with three categories to model the entity relations in VHF sentences, including pre‐training Chinese language model for initializing embedding from VHFDT, BiLSTM for rich features, and Multi‐head Attention for focusing on triples. In experimental part, this study uses Precision(P), Recall(R), and F1 to evaluate the accuracy and effectiveness of the proposed method and baseline models. According to experimental results, the proposed model efficiently extracts the key information from complex language environment and achieves better work on relational triple extraction than other baseline models. The model achieved an F1‐score of 83.2% on the overlapping testing data, which is an improvement of 1.8% compared to the second‐best baseline model. [ABSTRACT FROM AUTHOR]

With the development of the Internet, there has been a significant increase in various types of textual information. However, when people engage in the composition of formal texts, they often incorporate their colloquial habits, which can diminish the professionalism and formality of the text. Existing research on Chinese texts primarily focuses on correcting misspelt characters that are visually or phonetically similar, as well as obvious grammatical errors, such as redundancy, omissions, and incorrect word order. However, there is limited research addressing the correction of text that exhibits colloquial expressions without apparent grammatical errors or misspelt characters. This article proposes a novel technique that utilizes deep learning methods to directly transform colloquial textual expressions into formal written expressions. Firstly, a parallel corpus dataset of written and spoken language is constructed using a back-translation strategy. Then, an end-to-end learning mechanism based on neural machine translation is employed, with colloquial text as the source language and written text as the target language. This allows the model to directly transform the colloquial text into text with a formal style. Finally, an evaluation of the proposed approach is conducted using the bilingual evaluation understudy (BLEU) and manual assessment techniques. The experimental results demonstrate that the technology proposed in this paper performs well in the task of de-colloquialization in Chinese texts. The contribution of this paper lies in proposing an automated method for collecting a substitute for manually annotated parallel corpora of spoken and written language, which significantly saves time and reduces the manual cost of constructing the dataset. Furthermore, the application of end-to-end learning techniques from neural machine translation to the task of de-colloquialization allows the trained model to directly generate written language flexibly based on the input of spoken language. This presents a novel solution for the task of the de-colloquialization of Chinese text. [ABSTRACT FROM AUTHOR]

The practitioner-patient interview has been the subject of several studies in the world of medicine and in the field of teaching languages for specific purposes It has been considered one of the most critical oral genres in language teaching for medical-health purposes. Every health professional has to know and understand why the patient has come for a consultation; they must have the necessary skills to obtain as much information about the patient's health problem as possible, and if necessary, they have to perform a physical examination. The practitioner-patient interview is divided into several steps. Each step consists of a specific task with its specific objectives for the practitioner. For about fifteen years, the French Language Centre of McGill University, an English-speaking university in Montreal, Canada, has been offering French courses to students specializing in different areas of the Faculty of Health Sciences and Social Work who wish to do their clinical placements and pursue their professional career in the province of Quebec. Most of McGill’s students are native English speakers from different parts of Canada and the United States or international students whose first language is not necessarily English. One of the most important oral genres which must be taught to these students is the practitioner-patient interview in French, since one of their principal tasks as healthcare professionals will to interact with patients. Furthermore, students who have obtained a degree in any healthcare profession from an English-speaking university in the French-speaking province of Quebec must take a French language exam offered by the Office québécois de la langue française (OQLF). In one of the activities of this exam, the candidates must interview a patient or a caregiver in French. Therefore, this constitutes another reason to teach the practitioner-patient interview to our students. Unfortunately, there is little extant literature on how to teach students to carry out a practitioner-patient interview in French as a second language. Moreover, the possibility of recording actual interviews for use in class is practically impossible to respect patient confidentiality. This paper aims to share with the scientific community and with other language for specific purposes instructors how the practitioner-patient interview is taught at McGill University to non-native French speakers who wish to work in Quebec. [ABSTRACT FROM AUTHOR]

The purpose of this research paper is to identify and analyze word choice errors committed in spoken English by Saudi EFL students at King Khalid University. It also seeks to identify the origins of the errors and to suggest relevant solutions to assist learners in developing lexical competence and reducing the occurrence of these errors. The research focuses on five different forms of word choice errors. These errors consist of errors of redundancy, literal translation, subject/verb agreement, collocation, and inappropriate usage of formal and informal words. To gather data, the study is based on conducting structured interviews with three EFL-speaking teachers. It also employs a speaking test as a data collection technique from fifteen EFL learners chosen at random, who are required to speak one by one in front of the class for 3-6 minutes. Their oral performance is recorded by mobile phones. According to data analysis, the most common error category is literal translation (33.97%), which is followed by collocation errors (24.88%) as well as subject/verb agreement (18.66%). Furthermore, there are redundancy errors with a percentage of (12.44%). The last type of error committed is inappropriate use of formal and informal language, which has a percentage of (10.04%). The study attributes these errors to a variety of causes, including the Arabic language's impact as well as ineffective teaching techniques and learning strategies. The study offers several recommendations and pedagogical implications to help EFL learners overcome and minimize word choice errors. [ABSTRACT FROM AUTHOR]

College English teaching aims to cultivate students' comprehensive ability to use English. The study of spoken English barriers is a hot topic in this subject area. Based on a survey of non-English primary college students' spoken language impairments, this paper analyzes the research status of spoken language impairments at home and abroad. It relies upon the theoretical basis of Swain's output and Krashen's input hypotheses. With extensive data mining in colleges and universities as the entry point, this paper's content, object, and method are determined by combining qualitative and quantitative research with empirical research. Through the combination of classroom observations, questionnaires, interviews, and other research forms, this paper concludes that the spoken language barriers of non-English primary college students include language and non-language barriers. The influencing factors of the spoken language output barriers include subjective and objective aspects. The questionnaires are analyzed from the three dimensions of the students, schools, and education departments. This paper proposes some ways to overcome the spoken English barriers of non-English college students. It also suggests that non-English college teachers should pay more attention to the cultural transmission of English-speaking countries in English classes, cultivate students' cross-cultural awareness, and enhance students' enthusiasm in English learning. These actions are more conducive to overcoming the psychological barriers in spoken English output. [ABSTRACT FROM AUTHOR]

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In this paper, we propose an adversarial transfer learning method to solve the lack of data resources for named entity recognition (NER) tasks in spoken language understanding. In the framework, we use bi-directional long short-term memory with self-attention and conditional random field (BiLSTM-Attention-CRF) model which combines character and word information as the baseline model to train source domain and target domain corpus jointly. Shared features between domains are extracted by a shared feature extractor. This paper uses two different sharing patterns simultaneously: full sharing mode and private sharing mode. On this basis, an adversarial discriminator is added to the shared feature extractor to simulate generative adversarial networks (GAN) and eliminate domain-dependent features. This paper compares ordinary adversarial discriminator (OAD) and generalized resource-adversarial discriminator (GRAD) through experiments. The experimental results show that the transfer effect of GRAD is better than other methods. The F1 score reaches 92.99% at the highest, with a relative increase of 12.89%. It can effectively improve the performance of NER tasks in resource shortage fields and solve the problem of negative transfer. [ABSTRACT FROM AUTHOR]

This paper illustrates the role of phrasal verbs in business communication (a business environment). With the world's economy becoming increasingly global, the desire and ability to compete in the global economy are some of the greatest challenges facing business today. Organizations will want to negotiate, consider joint ventures, adapt products for an international market. All of this involves communication, which is crucial to business and, therefore, communication skills are vital. A discussion about business communication should consider a wide array of elements, such as: verbal communication, nonverbal communication, paralanguage (intonation, tone of voice, pitch), the personal psychological traits of the people involved in that business, their attitudes, perceptions and behavior, the speakers' status, their managerial qualities, qualifications, knowledge, skills and abilities, experience, interests and needs, the general economic, social and even political context. This paper mainly focuses on the use of phrasal verbs (and collocations) in this type of communication. [ABSTRACT FROM AUTHOR]

Computer-assisted language learning (CALL) systems increasingly arouse a significant interest and establish a presence in automated foreign language learning. They enhance traditional learning methods by providing access to various accents and spoken language styles through websites, mobile applications, and social media. Herein, mispronunciation detection is a key component mainly addressed as a classification problem. Meanwhile, deep learning (DL) advances have promoted these systems by training deep neural networks (DNN) to classify a pronunciation as correct or incorrect. However, the effectiveness of the DL models is hindered by many shortcomings, such as the scarcity of labeled data. To address this issue, the paper assumes an anomaly detection-based mispronunciation detection approach. It utilizes a variational autoencoder (VAE) relying on a density-based method to model the "normal data." The VAE is a generative model trained in a self-supervised way to learn the distribution of the correct pronunciations, standing for "normal data," and is expected to detect mispronunciations, standing for "abnormal data" during the test stage. Our proposition was evaluated in the context of Arabic pronunciation learning through the ASMDD Arabic dataset. The obtained results are promising, with an accuracy of about 98%. The proposed VAE outperformed the standard autoencoder as well as the state-of-the-art convolution neural networks used for Arabic mispronunciation detection. [ABSTRACT FROM AUTHOR]

Nowadays, the socio-cultural image of suicide indicates a greater acceptance of the act, as well as its linguistic neutralization. The discernible differences with respect to word-formation and definitions used in the spoken language seem to be particularly significant. The paper discusses this issue in relation to groups of students of medical studies (nursing, midwifery, medicine, emergency medical services), social studies (journalism) and specialists (academic employees, doctors, midwives and nurses, psychiatrists, psychiatric hospital employees). The analysis of atitudes towards suicide exhibited in spoken language was based on focus group interviews, which enabled the reconstruction of deeply rooted ways of thinking and perceiving phenomena in research-controlled conditions. The choice of words, topics, and the way of defining suicide, as well as related issues, reflected the state of knowledge about suicidology and (underlying) emotions or atitudes. [ABSTRACT FROM AUTHOR]

Learners of additional languages, particularly in adulthood and instructed settings, are typically exposed to large quantities of written input from the earliest stages of learning, with varied and far-reaching effects on L2 phonology. Most research investigating this topic focuses on learning across languages that share the same orthographic script, often involving the Latin alphabet and English. Without exploring phonological learning over a greater diversity of spoken and written language combinations, our understanding of orthographic effects on L2 phonology remains narrow and unrepresentative of the many individuals acquiring languages across writing systems, globally. This paper draws together preliminary research relating to the influence of written input, in a distinct script from known languages, on L2 phonology. Studies are grouped into those with naïve participants, where the written forms are entirely unfamiliar to the participant, and those with experienced learners, who have varying levels of proficiency and familiarity with the target orthography. While there is great scope and need for further investigation, initial evidence suggests that even entirely unfamiliar written input impacts phonological learning and is certainly influential with growing proficiency in the spoken and written language. The article concludes with theoretical and methodological considerations for future research in this emerging field. [ABSTRACT FROM AUTHOR]

We consider the problem of learned speech transmission. Existing methods have exploited joint source–channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate–distortion–perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source–channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication. [ABSTRACT FROM AUTHOR]

While there is a clear tendency for a Theme not to occur within its own tone group, it is equally clear that clauses are frequently formed out of more than a single tone group. Hence it is precipitous to assume an a priori relationship between clause and tone group/information unit. Speakers in pursuit of their individual communicative choices manage their interactional and informational needs by producing prosodic and thematic choices appropriate to their goals. Such choices as we have seen are frequently mutually re-enforcing, but speakers may employ prosodic choices which foreground their interactional needs. Specifically, this entails that there is no direct relationship between lexico-grammatical and prosodic meanings; both redound with semantic meanings. Yet, these meanings may be constrained by the interactional demands of the communicative situation in which a speaker operates. In this paper, I examine the spoken realisation of Interpersonal Theme and find that Interpersonal Themes realized by Mood adjuncts and metaphors of modality tended to be realized with different key. Similarly, the expectation that marked Theme was likely to attract high key was found to be subject to the speaker's need to manage the interaction. [ABSTRACT FROM AUTHOR]

Acoustic echo cancellation (AEC) methods aim to suppress the acoustic coupling for hands‐free speech communication. Traditional AEC works by identifying the acoustic impulse response using adaptive algorithms. With recent research advances, deep learning has become an attractive choice for AEC. This paper introduces a two‐stage bidirectional long short term memory (TS‐BLSTM) framework, incorporating multi‐head self‐attention mechanisms after each BLSTM block. This is aimed at better capturing contextual information and further enhancing ability of the model to handle complex acoustic scenarios. The BLSTM blocks are utilized to aggregate magnitude spectrum information, modelling both time and frequency dependencies. Additionally, dilation convolution is introduced to broaden the range of information in each convolution output. The magnitude decoder estimates a mask for the input, resulting in the generation of an estimated magnitude spectrum for near‐end speech. Experimental results indicate that the proposed method achieves promising outcomes. [ABSTRACT FROM AUTHOR]

Speech recognition is one of the most convenient forms of human beings engaging in the exchanging of information. In this research, we want to make robots understand human language and communicate with each other through the human language, and to realize man–machine interactive and humanoid– robot interactive. Therefore, this research mainly studies NAO robots’ speech recognition and humanoid communication between double-humanoid robots. This paper introduces the future direction and application prospect of speech recognition as well as its basic method and knowledge of speech recognition fields. This research also proposes the application of the most advanced method—establishment of the Hidden Markov Model (HMM) for the continuous word recognition in the speech recognition of NAO robots. In addition, this paper focuses on the establishment of a modelling algorithm and the extraction method of speech characteristics and the existing problems. Meanwhile, this research demonstrates the experiments of the NAO robot structured function and the voice interactive of double-humanoid robots. Through the use of an NAO-robot-controlled platform, Choregraphe software, and combined calling of Baidu speech recognition, the communication between double-humanoid robots has been achieved. Besides, a series of programming designs have realized the interactive functions such as NAO robots’ daily dialogue and communication, arithmetic function (addition, subtraction, multiplication, and division), singing, making movement, and rhetorical-pattern dialogues. Finally, it shows the significance and contribution of research of interaction between doublehumanoid robots. [ABSTRACT FROM AUTHOR]

Objective(s): The aims of this study were to: 1) discover location (by city) of contributors to poster and oral presentations at recent ANZSNM conferences; 2) determine the nuclear medicine themes most commonly explored; 3) establish institutions producing the highest number of oral and poster abstracts and 4) determine publication rates of conference abstracts to full papers from recent ANZSNM conferences. Methods: Retrospective analysis of abstracts published in the Internal Medicine Journal Special Issues 2014-2019 identified 614 abstracts. Invited plenary speaker abstracts were excluded. Descriptive statistics were used in data analysis. Conference abstracts were analysed using the following criteria: poster or oral presentation, author/s, city location, hospital and subject matter. Themes defined by the ANZSNM conference committee for abstract submission were: cardiology, oncology, neurology, therapy, renal/urology, gastrointestinal, paediatrics, musculoskeletal, infection/inflammation, technology, physics, radiation safety, radiopharmacy/radiochemistry, education, or general. Retrospective analysis of 555 conference abstracts (excluding New Zealand and International, 59 abstracts) using Google Scholar, Pubmed and Google databases was undertaken. Abstract titles, key words, institutions and/or authors' names were used to find peer-reviewed papers. Identified papers were authenticated through either open access, publicly available author information or Monash University's library access. Published paper citations were also recorded (up to 1st July 2019). Results: Analysis of 614 abstracts 2014 - 2019 was performed. Over five years, the average number of poster abstracts was 67.8 and oral 55.0. Sydney submitted the highest number of poster abstracts, while Melbourne the highest number of oral abstracts. Most popular abstract theme was oncology for both poster and oral abstracts. Publications found had in excess of 1250 citations. One hundred and one publications from one hundred and seven conference presentations were identified, distributed across sixty journals. Conference presentation to full publication rate was 18.2%; excluding 2019 conference abstracts the rate was 21.5%. Conclusion: Publishing research findings is a challenging process. A retrospective analysis of research presented at recent ANZSNM conferences by abstract content was undertaken, with conference presentation to full publication rate found to be at the lower end of reported literature findings. [ABSTRACT FROM AUTHOR]

The main goal of this Special Issue is to present the latest advances in research and novel applications of speech and language technologies based on the works presented at the IberSPEECH edition held in Barcelona in 2018, paying special attention to those focused on Iberian languages. IberSPEECH is the international conference of the Special Interest Group on Iberian Languages (SIG-IL) of the International Speech Communication Association (ISCA) and of the Spanish Thematic Network on Speech Technologies (Red Temática en Tecnologías del Habla, or RTTH for short). Several researchers were invited to extend their contributions presented at IberSPEECH2018 due to their interest and quality. As a result, this Special Issue is composed of 13 papers that cover different topics of investigation related to perception, speech analysis and enhancement, speaker verification and identification, speech production and synthesis, natural language processing, together with several applications and evaluation challenges. [ABSTRACT FROM AUTHOR]

This paper aims to explore the trends and hot topics of American applied linguistics by the content analysis of AAAL annual conferences' handbooks. Making a diachronic research on topical strands of AAAL Conference from 1999 to 2019, the paper studies the dynamics and the trends of American applied linguistics by analyzing the changes of the strands in the conferences under study in 20 years. Based on self-built corpora of titles and abstracts of the presented papers in 2019 AAAL Annual Conference, high-frequency vocabulary and their collocations are detected by TagCrowd and AntConc in order to find the current hot topics of applied linguistics research. The conclusion is that the development of applied linguistics has speeded up in the past four years. Educational linguistics, corpus linguistics, research methodology, teacher education, lexical research, phonetics/phonology and oral communication are the new trends in this field. The hot words of applied linguistics in 2019 include academic writing, teacher education, Chinese and translanguaging. Finally, the study found that retrospective review study of international conferences could be a new research method in the field of review study, providing scholars with a comprehensive understanding of the discipline dynamics over a period of time. [ABSTRACT FROM AUTHOR]

Differences in language use and structures between signed and spoken languages have often been attributed to so-called language "modality." Indeed, this is derived from the conception that spoken languages resort to both the oral-aural channel of speech and the visual-kinesic channel of visible bodily action whereas signed languages only resort to the latter. This paper addresses the use of enactment, a depictive communicative strategy whereby language users imitate referents in signed and spoken languages. Reviewing comparative research on enactment, this paper highlights theoretical and methodological shortcomings in prior works. First, a broader set of causal explanations needs to be taken into account when interpreting differences between signing and speaking communities. A more comprehensive conceptual toolbox ensures that differences are not automatically attributed to modality. In particular, lessstudied factors of language diversity, like sociolinguistic and cultural ecologies, and how they interact with other factors should be considered. Second, diversity in enactment across signed and spoken languages is shown to be inadequately and insufficiently documented. It is argued that by comparing enactment across more diverse signing and speaking communities and using large, directly comparable corpora, solid analyses can be carried out, enabling a better understanding of how and why different communities use enactment in similar or different ways. [ABSTRACT FROM AUTHOR]

Presents an article about orality and literacy in giving a paper at a scholarly conference. Difference between listening to someone speak extemporaneously and listening to someone read a written text; Aspect of reading aloud that results in semantically more dense discourse; Distinction between authorship and principality.

This study presents a novel web-based learning platform that leverages human-computer intelligent interaction to enhance English communication skills. The platform integrates cutting-edge technologies to create an immersive learning experience, combining natural language processing, speech recognition, and interactive exercises. Learners engage in real-time conversations with virtual tutors, receive personalized feedback, and access a vast repository of educational resources. The platform not only facilitates language acquisition but also encourages self-paced learning, making it a valuable tool for both educators and students. By harnessing the power of artificial intelligence, this web-based platform represents a significant advancement in the realm of English language education. To overcome these issues this paper proposed SVM with an improved satin Bower bird optimization algorithm (SVM-ISBBO). SVM-ISBBO uses fog computing services that minimize the latency and speeds up the process, effectively handling huge wearable devices. In this proposed work SVM-ISBBO monitors the students communication, vocal parameters, blood pressure, etc, and these values are obtained from wearable sensor devices and their notifications are sent back to teachers. Teachers diagnosed the student information and sent back the alert notifications to the students for taking proper medications. All this information is stored in fog-based cloud storage in a secure manner. The accuracy rate of KNN got 78.56%, NB got 81.74%, SVM got 85.15% and the proposed work of SVM-ISBBO got 92.34%. [ABSTRACT FROM AUTHOR]

Aim: To investigate spoken language comprehension (SLC), single‐word comprehension (SWC), functional communication development, and their determinants, in children with cerebral palsy. Method: This was a prospective cohort study in the Netherlands spanning 2 years 6 months. The main outcomes were SLC and SWC, assessed by the Computer‐Based instrument for Low motor Language Testing (C‐BiLLT) and the Peabody Picture Vocabulary Test‐III‐NL (PPVT‐III‐NL) respectively; and functional communication, measured by a subscale of the Focus on the Outcomes of Communication Under Six‐34 (FOCUS‐34). Linear mixed models were used to determine developmental trajectories, which were compared with norm and reference data. Potential determinants, for example intellectual functions, speech production, functional communication level (classified with the Communication Function Classification System, CFCS), and functional mobility, were added to assess their effects. Results: Children with cerebral palsy (n = 188; mean age 59 months, range 17–110) were monitored for 2 years 6 months. Developmental trajectories for SLC (C‐BiLLT) and SWC (PPVT‐III‐NL) were nonlinear; those for functional communication (FOCUS‐34) were linear. Compared with norm and reference groups, significantly delayed SLC, SWC, and functional communication development were found. Determinants for SLC and SWC were intellectual functions and functional communication level (CFCS); and for functional communication development (FOCUS‐34), speech production and arm–hand functioning. Interpretation: Children with cerebral palsy showed delayed SLC, SWC, and functional communication development compared with norm and reference groups. Remarkably, functional mobility was not associated with the development of SLC, SWC, or functional communication. What this paper adds: Children with cerebral palsy have delayed spoken language comprehension (SLC), single‐word comprehension (SWC), and functional communication development, compared to norm and reference data.Determinants for SLC and SWC development are intellectual functions and functional communication level.Determinants for functional communication development are speech production and arm–hand functioning.Functional mobility is not associated with SLC, SWC, or functional communication. What this paper adds: Children with cerebral palsy have delayed spoken language comprehension (SLC), single‐word comprehension (SWC), and functional communication development, compared to norm and reference data.Determinants for SLC and SWC development are intellectual functions and functional communication level.Determinants for functional communication development are speech production and arm–hand functioning.Functional mobility is not associated with SLC, SWC, or functional communication. Video Podcast: https://youtu.be/x09Tac7tsz0 [ABSTRACT FROM AUTHOR]

This study presents a review of interventions targeting the oral pragmatic language abilities of children with developmental language disorder (DLD), as well as an overview of the ingredients and teaching techniques used to provide changes, determine the setting and dosage, test the efficacy of the intervention, and measure the quality of the studies. The review adheres to regulations outlined in the PRISMA guidelines for systematic reviews. Seven electronic databases were searched using appropriate search terms. Only single-case studies (AB, ABA, interrupted time series, alternating treatment, multiple baseline designs, and non-experimental case designs) were included. All included papers were published between 2006 and 2020. The age range of participants was 3 to 18 years and included participants who were either formally diagnosed with DLD or met the criteria for language disorder. Studies where oral pragmatic language was measured as an outcome variable were included and appraised using Risk of Bias Assessment tool for Non-randomised Studies (RoBANS). Eleven studies were included in the final analysis: these studies reported on interventions in the domain of pragmatic language for children with DLD aged 3 to 9 years. Seven of the interventions targeted conversational skills, while the remaining four targeted narrative skills. All studies used prompting or modelling techniques to enhance pragmatic abilities selected for change such as initiation, response, turn-taking, topic management, adaption to listener knowledge, and the ability to build sequences. Several of the children in the studies improved their pragmatic skills after undergoing the intervention, however individual differences were noted. All interventions were carried out in the children's schools and varied in terms of duration. The quality of the studies included was medium. Due to the diversity observed among the studies regarding oral pragmatic outcome measures that were used to evaluate changes, as well as the ingredients and teaching techniques used, it was difficult to draw precise conclusions about recommendations for practitioners based on the included studies. Although several interventions reported moderate changes in conversational and narrative skills, as well as good social validity, the findings are considered weak because of the lack of ecological validity. [ABSTRACT FROM AUTHOR]

This paper aims to prove a deep connection between language and thought and confirm that language is a powerful tool that humans possess. It is not just a collection of words, but rather a medium of communication that has far-reaching impacts. Effective communication heavily relies on language, which serves as a vital tool for communicating with others. Although it may not determine our thought patterns, language is universally recognized as an essential component of human interaction. With over 7,000 languages spoken worldwide, each one boasts a unique vocabulary, sound, and structure. Humans possess an incredible ability to convey complex ideas and thoughts through language. Language can also impact our perception of time and color. Different languages have varying words to describe colors, with some having only a few, such as dark and light. This article explores these various aspects of language. The connection between language and thought is intricate and multi-dimensional. Language is an essential aspect of human interaction, and its structure is linked to brain processes and external factors in nature. This article intends to discuss the varying meanings of language expressions and their significance. [ABSTRACT FROM AUTHOR]

In the era of modernism, the natural symbols of art - expressed through aesthetic elements - have been seen replaced by the verbal notations of communication. The replacement forced the postmodernists to deconstruct the concept of modernism to bring back the notion of symbolic art superficially and to revitalize the meaning of art and its cohesive presence in the built environment. The revitalization process, however, does not seem to have gone without raising questions in the academic community. Does the aesthetic impulse come from the structural spirit of a built form alone? Is just aesthetics deeply rooted in built-form identity? Is aesthetics not associated with the social environment and economic living? Can aesthetics exist in isolation? Can aesthetics be more of a by-product of functionality than the product itself? Using the works of Harries and Eisenman, the paper develops a review sketch exploring these questions. Many other attributes, such as aesthetics production, aesthetics generation, and environmental aesthetics, and their roles in art appreciation have ensured positions in the discussion. The conclusions seem to warn that the influence of social co-existence in defining built-form aesthetics in the postmodern era and later, divorced from reality - avoiding the presence of the different layers in the social fabric and their relationships among themselves - seldom helps to produce any futuristic vision but invites chaos in thoughts and perceptions crossing over between studies and practices in architecture. [ABSTRACT FROM AUTHOR]

Copyright of Acta Linguistica Asiatica is the property of Acta Linguistica Asiatica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CIRCULO de Linguistica Aplicada a la Comunicacion is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Educational systems have the common objective of coordinating efforts to improve students' communicative competence and facilitate the development of their oral and written language skills for personal, social, academic, and professional purposes. The work on communicative competence from a school perspective has various antecedents in the international educational context. In Spain, the improvement of students' linguistic communication competence in all the languages spoken in the schools (mother languages and second languages) has been addressed through different initiatives, among which the SLP (School Language Project) programme, better known in Spanish as Proyecto Lingüístico de Centro (PLC), which has been implemented in the Autonomous Community of Andalusia since the 2013–2014 academic year, stands out. The aim of this paper is to examine the opinions of the teacher coordinators involved in the implementation of SLP in their schools as well as their perceptions about the impact of external factors in the process and, lastly, to highlight the main implications emerging from this global vision for the implementation of interdisciplinary projects to improve LCC (Linguistic Communication Competence) schools. This study is based on the feedback provided by the coordinators of the SLP initiative in Andalusian institutions over five academic years. The findings have been studied utilising a qualitative methodology and assessed using the Atlas Ti 6.0 software, then grouped into five diverse categories. The results indicate that, despite the programme's strengths, specific improvement measures are necessary in both external and internal aspects related to SLP. [ABSTRACT FROM AUTHOR]

This paper is about very interesting elements found in the daily life: signs, symbols and pictograms. From the sunrise till sunset, the people is surrounded by these things which may help to have an easier life. We have learned a lot about the capacity of these elements to act as a main guide, using many times just graphics. We have understood that signs, symbols and pictograms must catch the people's attention. These ones have to guide, allow, warn or forbidden something, inform or assure the final destination for a traveler. We have identified the typology of these graphic representations and the working process to create pictograms. Some findings and results are explained in the paper. [ABSTRACT FROM AUTHOR]

Spoken English practice requires a combination of listening, speaking, reading, and writing, among which listening and speaking are the most difficult. In order to improve the speaking ability of the practitioner, the pronunciation of spoken English needs to be corrected in time. However, the workload of manual evaluation is too large, so it is necessary to combine intelligent methods for spoken language recognition. Based on the needs of spoken English pronunciation correction, this paper combines the computer English speech recognition technology to construct the spoken English recognition and correction model and combines the coding technology to study the English speech recognition technology. Moreover, this article constructs the spoken English practice system based on the actual needs of spoken English practice. Finally, this paper verifies the reliability of this system through experimental research, which provides a reliable means for the subsequent intelligent learning of spoken English. [ABSTRACT FROM AUTHOR]

Copyright of Akofena is the property of Universite Felix Houphouet Boigny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spoken language recognition has made significant progress in recent years, for which automatic speech recognition has been used as a parallel branch to extract phonetic features. However, there is still a lack of a better training strategy for such architectures of two individual branches. In this paper, we analyze the mostly used two-stage training strategies and reveal a trade-off between the recognition accuracy and the generalization ability. Based on the analysis, we propose a three-stage training strategy and an orthogonality regularization method. The former adds a multi-task learning stage to the traditional two-stage training strategy to extract hybrid-level and noiseless features, which can improve the recognition accuracy on the basis of maintaining the generalization ability, while the latter constrains the orthogonality of base vectors and introduces prior knowledge to improve the recognition accuracy. Experiments on the Oriental Language Recognition (OLR) dataset indicate that these two proposed methods can improve both the language recognition accuracy and the generalization ability, especially in complex challenge tasks, such as cross-channel or noisy conditions. Also, our model, which combines these two proposed methods, performs better than the top three teams in the OLR20 challenge. [ABSTRACT FROM AUTHOR]

Distance learning with its virtual settings has launched the transformation of education delivery, pedagogy, and classroom management. Consequently, it could cause educational discourse transformation in higher education as well. The research is focused on revealing educational discourse transformation in distance learning. Besides, the paper answers the following research questions: how university teachers communicate with students in virtual classrooms; what language patterns they use for giving instructions; whether verbal communication between teachers and students has crucially changed in digitally-based education. A mixed method approach (discourse analysis of 12 video-recorded lessons of ESL university teachers and the survey of students (n = 45)) makes it possible to shed light on the state of play in virtual educational discourse. The paper reveals that 1) communication in virtual classrooms depends on pedagogical activities which allows classifying educational language models into the following categories – preparation of teaching material presentation online, student engagement in an activity online, giving instruction on how to use EdTech, and expressing emotions or attitude; 2) university teachers use special language patterns for giving instructions related to using ICT tools and apps, e.g. screen sharing, chatting and emotional modality; 3) verbal communication between teachers and students except for linguistic and extra-linguistic features includes informal modes of communication – chat emojis and signs. The findings highlight that educational discourse has crucially changed in digitally-based education and has transformed into virtual educational discourse. The insights of the paper can contribute to the investigation of virtual educational discourse analysis. [ABSTRACT FROM AUTHOR]

The oral English test in domestic universities requires teachers to modify a large number of candidates' oral recordings. This is the work of using time repeatedly. Using the CALL system to realize the automation of conversation recording can reduce the burden of teachers' work. Therefore, it is of great practical significance to develop an automatic and accurate scoring system for oral English. With the development of artificial intelligence, deep learning technology has been gradually applied in various fields. Similarly, in the application of oral scoring, deep learning technology makes the implementation of such a system possible. Based on the deep learning technology, this paper proposes an automatic scoring algorithm for spoken language and implements a detailed design and evaluation system. The system consists of two modules. The pronunciation standard of spoken pronunciation and the content of spoken pronunciation are scored, and the sum of these two scores is the final score. Finally, this paper uses 650 oral English recordings from a college English test to train the artificial neural network. Experimental results show that if the training data set is small, the BP network model can obtain better comprehensive evaluation performance. [ABSTRACT FROM AUTHOR]

This paper studies the influence of executive verbal communication on the convergence of investors’ opinions, defined as reduction of differences in investors’ valuations. Building on the corporate communication and sense-giving literatures, we argue that executive verbal communication impacts investor opinion convergence through its influence not only on disclosure of new information, but also on the comprehensibility and credibility of the information conveyed. Accordingly, we hypothesize that the newness, simplicity, and unscriptedness of executive verbal communication are all positively associated with investor opinion convergence. We also argue that the effect of executive communication on investor opinion convergence will be amplified when investors have a greater demand for information as would be the case for those firms with higher earnings uncertainty. Using a unique research design and second-by-second intraday stock trading data from 10,415 conference calls of 847 publicly listed firms between 2005 and 2012, we found strong support for our predictions. [ABSTRACT FROM AUTHOR]

Zero frequency is a fundamental problem in information retrieval using language models and smoothing is applied to deal with this problem. The cluster-based smoothing method is found to be effective for information retrieval using language models. Since the effectiveness of cluster-based smoothing depends on clustering quality, there is scope for improvement by enhancing the clustering algorithm. In this paper, we present a study on how to improve cluster-based smoothing using a histogram-based incremental clustering algorithm and word embeddings. To our knowledge, this is the first study on the cluster-based smoothing method which is integrated with a language model for developing an effective IR system for the Bengali language which is one of the most spoken Indian languages. The proposed method has been tested on two benchmark Bengali IR datasets. The experimental results show that our proposed model for Bengali document retrieval is effective and it outperforms several baseline IR models. [ABSTRACT FROM AUTHOR]

The language learning field is not exempt from benefiting from the most recent techniques that have revolutionised the field of speech technologies. L2 learning, especially when it comes to learning some of the most spoken languages in the world, is increasingly including more and more automated methods to assess linguistics aspects and provide feedback to learners, especially on pronunciation issues. On the one hand, only a few of these systems integrate automatic speech recognition as a helping tool for pronunciation assessment. On the other hand, most of the computer-assisted language pronunciation tools focus on the segmental level of the language, providing feedback on specific phonetic pronunciation, and disregarding the suprasegmental features based on intonation, among others. The current review, based on the PRISMA methodology for systematic reviews, overviews the existing tools for L2 learning, classifying them in terms of the assessment level, (grammatical, lexical, phonetic, and prosodic), and trying the explain why so few tools are nowadays dedicated to evaluate the intonational aspect. Moreover, the review also addresses the existing commercial systems, as well as the existing gap between those tools and the research developed in this area. Finally, the manuscript finishes with a discussion of the main findings and foresees future lines of research. [ABSTRACT FROM AUTHOR]

Students may encounter problems concentrating during a lecture due to various reasons, which can be related to the educator's accent or the student's auditory difficulties. This may lead to reduced participation and poor performance in the class. In this paper, we explored whether the incorporation of the humanoid robot Pepper can help in improving the learning experience. Pepper can capture the audio of a person; however, there is no guarantee of accuracy of the recorded audio due to various factors. Therefore, we investigated the limitations of Pepper's speech recognition system with the aim of observing the effect of distance, age, gender, and the complexity of statements. We conducted an experiment with eight persons including five females and three males who spoke provided statements at different distances. These statements were classified using different statistical scores. Pepper does not have the functionality to transcribe speeches into text. To overcome this problem, we integrated Pepper with a speech-to-text recognition tool, Whisper, which transcribes speech into text that can be displayed on Pepper's screen using its service. The purpose of the study is to develop a system where the humanoid robot Pepper and the speech-to-text recognition tool Whisper act in synergy to bridge the gap between verbal and visual communication in education. This system could be beneficial for students as they will better understand the content through the visual representation of the teacher's spoken words regardless of any hearing impairments and accent problems. The methodology involves recording the participant's speech, followed by its transcription to text by Whisper, and then evaluation of the generated text using various statistical scores. We anticipate that the proposed system will be able to increase the student's learning experience, engagement, and immersion in a classroom environment. [ABSTRACT FROM AUTHOR]

Currently, not all children that need speech therapy have access to a therapist. With the current international shortage of speech–language pathologists (SLPs), there is a demand for online tools to support SLPs with their daily tasks. Several online speech therapy (OST) systems have been designed and proposed in the literature; however, the implementation of these systems is lacking. The technical knowledge that is needed to use these programs is a challenge for SLPs. There has been limited effort to systematically identify, analyze and report the findings of prior studies. We provide the results of an extensive literature review of OST systems for childhood speech communication disorders. We systematically review OST systems that can be used in clinical settings or from home as part of a treatment program for children with speech communication disorders. Our search strategy found 4481 papers, of which 35 were identified as focusing on speech therapy programs for speech communication disorders. The features of these programs were examined, and the main findings are extracted and presented. Our analysis indicates that most systems which are designed mainly to support the SLPs adopt and use supervised machine learning approaches that are either desktop-based or mobile-phone-based applications. Our findings reveal that speech therapy systems can provide important benefits for childhood speech. A collaboration between computer programmers and SLPs can contribute to implementing useful automated programs, leading to more children having access to good speech therapy. [ABSTRACT FROM AUTHOR]

In recent decades, a very modest number of papers on nonverbal behavior in sales and the service sector have affected the field of study. Nevertheless, a comprehensive conceptual model is missing. Very heterogenous views appear to shape the topic. Scholars have shown that empathy is a crucial factor in interpersonal interaction. In particular, this applies to salesperson-customer interaction. Additionally, researchers found that nonverbal behavior shapes 90 % of mutual understanding and verbal communication hardly has an influence. However, salespersons' nonverbal behavior has received scant attention in marketing research. This study contributes to the corpus of knowledge by evaluating how the existent research is founded on various intellectual frameworks and by identifying the most important references, authors, themes, and journals, as well as comprehending it in a conceptual model of salespersons nonverbal behavior. To achieve this objective, I examined more than 3,000 publications published between 1994 and 2021 using bibliometric methods. This is a small body of literature compared to other fields. I determined four publishing eras that affected the evolution of research in this field. Nonverbal behavior has been studied from a range of angles in the publications that have published these articles. The predominant emphasis is on the United States and empathy as a central trait, offering scholars the ability to conduct further cross-national and cross-industry research. My study not only provides intellectual and social frameworks, but integrates them in a conceptual model. Thus, it lays the foundation for future study. [ABSTRACT FROM AUTHOR]

It is generally assumed that what we hear in our head is what we think and that, when we tell a thought to somebody else, the other person understands what our thought has been. This paper analyzes how we think and what happens when we communicate our thoughts verbally to others and to ourselves. The assumption that we become conscious in language is erroneous: verbal communication is only an intermediary. The conscious experience of verbal communication is a sensory phenomenon. We think through sensory images (see Part I). This natural way of thinking, is a very refined and accurate method of translating thought into consciousness. It expresses our essentially unconscious neural cognitive activity in conscious sensory images: visual thinkers 'see' what they have thought. Why humans use verbal communication to express their thoughts to themselves is difficult to understand as the verbal way is extremely limited. The complex parallel cognitive activity has to be encoded into language tokens which are positioned sequentially as a string of symbols which somehow must express something comparable. Talking to oneself is directed to an imaginary person who is assumed to be the talking person himself. This imaginary person develops with the inner voice in infants and when the child grows up, that imaginary person remains there, somebody he talks to when he thinks and to which he attributes his feelings and his actions. The imaginary person is experienced as the human Self, but actually verbalizes the thoughts of the natural – animal – Self. [ABSTRACT FROM AUTHOR]