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This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics. This paper documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them.

METANET 4 U is a European project aiming at supporting language technology for European languages and multilingualism. It is a project in the META-NET Network of Excellence, a cluster of projects aiming at fostering the mission of META, which is the Multilingual Europe Technology Alliance, dedicated to building the technological foundations of a multilingual European information society. This paper describe the resources produced at our lab to provide Synthethic voices. Using existing 10h corpus for a male and a female Spanish speakers, voices have been developed to be used in Festival, both with unit-selection and with statistical-based technologies. Furthermore, using data produced for supporting research on intra and inter-lingual voice conversion, four bilingual voices (English/Spanish) have been developed. The paper describes these resources which are available through META. Furthermore, an evaluation is presented to compare different synthesis techniques, influence of amount of data in statistical speech synthesis and the effect of sharing data in bilingual voices

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them.

Deliverable D2.4 del projecte METANET4U (Project CIP #270893)

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them. Peer Reviewed _

This paper describes the BUCEADOR search engine, a web server that allows retrieving. multimedia documents (text, audio, video) in different languages. All the documents are translated into the user language and are presented either as text (for instance, subtitles in video documents) or dubbed audio. The user query consist in a sequence of keywords and can be typed or spoken. Multiple Spoken Language Technologies (SLT) servers have been implemented, such as speech recognition, speech machine translation and text-to-speech conversion. The platform can be used in the four Spanish official (Spanish, Basque, Catalan and Galician) and in English.

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them.

This paper deals with the creation of multiple voices from a Hidden Markov Model based speech synthesis system (HTS). More than 150 Catalan synthetic voices were built using Hidden Markov Models (HMM) and speaker adaptation techniques. Training data for building a Speaker-Independent (SI) model were selected from both a general purpose speech synthesis database (FestCat;) and a database design ed for training Automatic Speech Recognition (ASR) systems (Catalan SpeeCon database). The SpeeCon database was also used to adapt the SI model to different speakers. Using an ASR designed database for TTS purposes provided many different amateur voices, with few minutes of recordings not performed in studio conditions. This paper shows how speaker adaptation techniques provide the right tools to generate multiple voices with very few adaptation data. A subjective evaluation was carried out to assess the intelligibility and naturalness of the generated voices as well as the similarity of the adapted voices to both the original speaker and the average voice from the SI model.

In this paper, multidialectal acoustic modeling based on shar- ing data across dialects is addressed. A comparative study of different methods of combining data based on decision tree clustering algorithms is presented. Approaches evolved differ in the way of evaluating the similarity of sounds between di- alects, and the decision tree structure applied. Proposed systems are tested with Spanish dialects across Spain and Latin Amer- ica. All multidialectal proposed systems improve monodialectal performance using data from another dialect but it is shown that the way to share data is critical. The best combination between similarity measure and tree structure achieves an improvement of 7% over the results obtained with monodialectal systems.

Some Speech Enhancement algorithms based on the iterative Wiener filtering Method due to L1m-Oppenheim [2] are presented. In the original Lim-Oppenheim algorithm, speech AR estimation is carried out using classic second-order analysis, but our algorithms consider a more robust AR modelling. Two different strategies of speech AR estimation are presented and both estimators are trying to see as less amount of noise as possible. First one uses a previous One-Sided Autocorrelation computation, that is a pole-preserving function, and the actual SNR m the second-order LPC analysis is increased. Second one combines advantages of Higher-Order Statistics [1] with a linear combination of AR coefficients, belonging to two consecutive overlapped frames, to assess a less disturbed speech estimation.

Aquest material didàctic de caràcter experimental s'adreça a aquells alumnes que s'introdueixen en l'estudi dels senyals i els sistemes analògics tant en el domini temporal com en el de freqüència. Els seus continguts han estat desenvolupats per professorat de l'assignatura Senyals i Sistemes I, que s'imparteix en els estudis que ofereix l'ETSETB. El llibre es divideix en quatre temes. El primer (Introducció als senyals i sistemes) se centra en l'anàlisi dels senyals i sistemes anàlogics i fa èmfasi especialment en els sistemes lineals i invariants. El segon (Transformada de Fourier) estudia la caracterització dels senyals i els sistemes en el domini de la freqüència, a partir de l'anàlisi de la transformada de Fourier i les seves propietats fonamentals. El tercer (Filtres) presenta les quatre tècniques clàssiques de disseny de filtres analògics pel que fa a la funció de transferència. Finalment, el tema quart (Correlació i Espectre) introdueix el concepte de correlació per senyals deterministes. El conjunt de l'obra inclou tant el llibre de continguts experimentals com un programari de simulació. L'estructuració del llibre de continguts experimentals presenta, al principi de cada capítol i també de forma intercalada, resums de la matèria teòrica en què se'n ressalten els conceptes més importants. Després d'aquests resums s'inclouen enunciats d'exercicis, problemes i pràctiques, agrupats segons la temàtica i ordenats segons el grau de dificultat.

This paper introduces a first approach to emotion recognition using RAMSES, the UPC’s speech recognition system. The approach is based on standard speech recognition technology using hidden semi-continuous Markov models. Both the selection of low level features and the design of the recognition system are addressed. Results are given on speaker dependent emotion recognition using the Spanish corpus of INTERFACE Emotional Speech Synthesis Database. The accuracy recognising seven different emotions—the six ones defined in MPEG-4 plus neutral style—exceeds 80% using the best combination of low level features and HMM structure. This result is very similar to that obtained with the same database in subjective evaluation by human judges.

This paper deals with a description of NaniTrans, a tool for segmentation and labeling of speech. The tool is programmed to work on the MATLAB application interface, in any of the supported platforms (Unix, Windows, Macintosh). The tool has been designed to annotate large speech databases, which can be also partially preprocessed (but require manual supervision). It supports the definition of an environment of annotation: set of annotation levels (orthographic, phonetic, etc.), display mode (how to show information), graphic representation (waveform, spectrogram), keyboard short-cuts, etc. This configuration is then used on a speech database. A safe file locking system allows many annotators to work concurrently on the same speech database. The tool is very friendly and easy to use by non experienced annotators, and it is designed to optimize speed using both keyboard and mouse. New options or speech processing tools can be easily added by using any MATLAB or user defined function.

Se presenta en este artículo un material docente para la enseñanza de las señales y los sistemas analógicos. El enfoque que se ha dado es eminentemente aplicado. Se presenta un conjunto muy completo de ejercicios, problemas y simulaciones que se complementan mutuamente. A parte del contenido y su estructuración, la principal característica de este material reside en el software desarrollado que explota de forma exhaustiva las posibilidades gráficas de MATLAB. Se proporciona una interfície muy amigable y potente para realizar las experiencias que se proponen.

Under the SpeechDat specifications, the Spanish member of SpeechDat consortium has recorded a Catalan database that includes one thousand speakers. This communication describes some experimental work that has been carried out using both the Spanish and the Catalan speech material. A speech recognition system has been trained for the Spanish language using a selection of the phonetically balanced utterances from the 4500 SpeechDat training sessions. Utterances with mispronounced or incomplete words and with intermittent noise were discarded. A set of 26 allophones was selected to account for the Spanish sounds and clustered demiphones have been used as context dependent sub-lexical units. Following the same methodology, a recognition system was trained from the Catalan SpeechDat database. Catalan sounds were described with 32 allophones. Additionally, a bilingual recognition system was built for both the Spanish and Catalan languages. By means of clustering techniques, the suitable set of allophones to cover simultaneously both languages was determined. Thus, 33 allophones were selected. The training material was built by the whole Catalan training material and the Spanish material coming from the Eastern region of Spain (the region where Catalan is spoken). The performance of the Spanish, Catalan and bilingual systems were assessed under the same framework. The Spanish system exhibits a significantly better performance than the rest of systems due to its better training. The bilingual system provides an equivalent performance to that afforded by both language specific systems trained with the Eastern Spanish material or the Catalan SpeechDat corpus.

El presente libro, formado por un manual de estudio y un manual de prácticas, ofrece una introducción al tratamiento digital de la señal que combina el estudio teórico con la posibilidad de experimentar con señales analógicas y digitales los conceptos aprendidos. El texto va acompañado por un disquete que incluye el programa 62, una herramienta interactiva que permite trabajar con señales y sistemas discretos, y las secuencias y los sistemas empleados en los ejemplos y los ejercicios que forman parte integrada del manual de estudio. El mismo programa 62 es el útil de laboratorio que el manual de prácticas requiere para el empleo de tratamiento digital de la señal en un entorno analógico. Este libro, dirigido inicialmente a los estudiantes de Señales y Sistemas II de la ETSETB, proporciona todo el material de estudio necesario para seguir la asignatura (texto de teoría, prácticas de laboratorio y colección de problemas) con una visión integrada y con un marcado carácter experimental.

This paper describes NaniBD, a set of tools designed for transcribing and validating speech databases, developed at the Signal Processing Group (GPS) of the Department of Signal Theory and Communications of the Polytechnic University of Catalonia (TSC/UPC). The main purpose of its development was the need of a revision system in order to validate and annotate the Spanish corpus of SpeechDat (II) in the speech processing environment available at GPS. Despite of this, NaniBD is designed as a general-purpose system that might fit any other database, idiom or speech processing system. So far, the system has been used to revise some 200,000 speech files from three different corpora. In this paper we will focus our attention to the actual implementation used in the transcription of a SpeechDat (II) specifications compatible Catalonian corpus. 1000 speakers, each of them uttering 44 files, compose this corpus. In this application, we use speech-noise detection, automatic recognition of spontaneous prompts, digit and letter to text translation and access to an external database in order to minimise the amount of time spent by human operators in the revision procedure.

It is well known that canonical Spanish, the dialectal variant `central' of Spain, so called Castilian, can be transcribed by rules. This paper deals with the automatic grapheme to phoneme transcription rules in several Spanish dialects from Latin America. Spanish is a language spoken by more than 300 million people, has an important geographical dispersion compared among other languages and has been historically influenced by many native languages. In this paper authors expand the Castilian transcription rules to a set of different dialectal variants of Latin America. Transcriptions are based on SAMPA symbols. The paper includes an identification of sounds that doesn't appear in Castilian, extend accepted SAMPA symbols for Spanish (Castilian) to different dialectal variants, describes the necessary rules to implement an automatic Orthographic to Phonetic transcription in several dialectal Spanish variants and show some quantitative results of dialectal differences.

We study some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim-Oppenheim [2], where the AR spectral estimation of the speech is carried out using a second-order analysis. But in our algorithms we consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors, where information of previous speech frames is taken to initiate speech AR modelling of the current frame. Two parameters are introduced to dessign Wiener filter at first iteration of this iterative algorithm. These parameters are the Interframe Factor (IF) and the Previous Frame Iteration (PFI). A detailed study of them shows they allow a very important noise suppression after processing only first iteration of this algorithm, without any appreciable increase of distortion. Finally, the simplest cumulant-based algorithm is applied to Speech Recognition and some preliminary results are presented.

Some Speech Enhancement algorithms based on the iterative Wiener filtering Method due to LimOppenheim [2] are presented. In the original Lim-Oppenheim algorithm, AR spectral estimation of speech is carried out using a second-order analysis, but our algorithms consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors. Information of previous speech frames is taken to initiate speech AR modeling of the current frame and, so, two parameters are introduced to dessign Wiener Filter at first iteration of every frame. Another algorithm obtains speech AR estimation in the autocorrelation domain. Both algorithms are compared to classical second-order algorithm (AR2) and third-onler cumulant-based algorithm (AR3), when car noise disturbs clean speech signal. A detailed study shows that boths techniques significantly increase noise suppression after first iteration processing and, therefore, convergence speed of this iterative algorithm is strongly accelerated.

In this paper we are addressed to the problem of speech presence and silences detection. Speech signals possess properties to which higher order statistics are sensitive. Bispectral-based statistics have been proved a good alternative [3] to energy-based statistics in a speech presence detector scheme, but at the cost of computations. Three alternative methods are proposed for a reliable and more computationally efficient detection. Two, lag-and frequency-domain, methods for the computation of the IT and the OT contribution to total skew while keeping the ability of the statistic in [3]. And a method based in the integrated polyspectrum (IP).

The performance of existing speech recognition systems degrades rapidly in the presence of background noise when training and testing cannot be done under the same ambient conditions. The aim of this paper is to propose the application of a simple multilabeling method, instead of the standard vector quantization -so called labeling-, as the front end for a speech recognizer based on the Vector Quantization (VQ) and Hidden Markov Models (HMM) approaches in order to increase its robustness to noise. Furthermore, not only cepstrum but also other features such as energy and dynamic parameters are evaluated and quantized independently in the multilabeling stage to represent more accurately characteristics of speech. The result of this process is a multiple multilabeling. Experimental results in the presence of additive white noise clearly demonstrate its good performance in isolated word recognition in noisy environments.

In this paper is made a comparison between sorne quantizers applied in a Multipulse Speech coder. Vector quantizer is compared against scalar quantization in the LPC parameter in the short predictor. Adaptive Multistage Vector Quantizer gives the best results. Long predictor coefficient obtained either in open or closed loop is quantized and results are compared. Finally, excitation signa! is quantized scalar and vectorially and results are compared. Any of the different VQ squemes tested gives a better performance in SNR terms than the scalar squeme

Most of the speech analysis methods developed up to date have been based on the autocorrelation function or power spectrum, i. e., the second order statistics of the signa!. In this paper it is shown that higher order statistics (cumulants) are also very useful in the study and characterization of the speech. A new pitch determination algorithm based on third order statistics is presented and tested in noise corrupted speech, at different levels of signa! to noise ratio, and with different kinds of noise. The results show that this new algorithm give a much better estimation of the pitch that the conventional autocorrelation method in al! the cases.

Spectral analysis techniques are used to bearing estimation problem. Each one of this gives a different array beamforming. We show here a generalized normalized Maximum Likehood Method which present a high resolution comparable to the singular value decomposition methods, but with a smaller computational load .

En estos últimos años, se han dedicado muchos esfuerzos al estudio de funciones asociadas como el módulo y la fase de la Transformada de Fourier para reducción de ancho de banda. En este trabajo se estudia la importancia de la envolvente y la frecuencia instantánea en problemas de análisis y síntesis. Los autores presentan un método para parametrizar la envolvente y la frecuencia instantánea de una señal real. Este método está muy relacionado con los de análisis espectral en el sentido de que bajo determinados aspectos, el dominio temporal y el dominio frecuencial pueden ser analizados en la misma forma.

This communication reports the use of demisyllables for continuous speech recognition in a specific application: the recognition of Spanish numbers. After a brief outline of the recognition system, a description of demisyllable syntactic constraints and one-speaker reference generation is provided. Finally, the recognition performance is assessed by means of two experiments: the recognition of integer numbers from zero to one thousand and telephone numbers uttered in a Spanish way (strings of integers from zero to ninety nine), in both applications the results that the system yielded were excellent.

During the last decade, many efforts have been devoted to the relative importance of associated functions like magnitude and phase of Fourier Transforms in image and signal bandwidth reduction. The reported work deals with the importance of the real envelope and instantaneous frequency in signal analysis/sintesis problems. In this paper authors show a method to parametrize the envelope and instantaneous frequency of a real signal. This method is very closed to spectral analysis methods in the sense that with an appropiate study, time domain and frequency domain can be analyced in a similar way.

This work describes a coder that works at bit rates of 9.6Kbits/s to 32 Kb/ s. Basically the system consist on a waveform DPCM coder with an improvement in the quantizer. This new feature consist on quantizing the prediction error taking into account its waveform characteristics. The prediction residual of a voiced signal is characterized by an energy sychronous with pitch. The envelope of this signal holds this information and can be used to quantize properly the prediction error. In this work a parametric version of the residual envelope is used in two ways: Dynamic bit assignment in time domain and adaptive control of the dynamic range of the quantizer.

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them.

In this paper authors show the importance of envelope and instantaneous frequency ~n analys~s and synthesis problems. Signa! representation in envelope and instantaneous pha se is introduced. Analogy between this representation and modulus/phase of the Fourier -· - Transform is shown in arder to preve that well known spectral analysis techniques can be used in envelope parametric representat·ion. Instantaneous phase is sampled not uniformly.

This paper deals with the application of Higher Order Statistics (HOS) in speech coding. A new model for speech production is implemented and the parameters of the vocal track are obtained from a linear combination of third order cumulants of the speech signal. Noisy signals have been used to test the system including colored Gaussian noise and sinusoidal interfering signals. Pitch and voicing decision are also obtained by HOS analysis of speech.

This paper presents a web-based multimedia search engine built within the Buceador (www.buceador.org) research project. A proof-of-concept tool has been implemented which is able to retrieve information from a digital library made of multimedia documents in the 4 official languages in Spain (Spanish, Basque, Catalan and Galician). The retrieved documents are presented in the user language after translation and dubbing (the four previous languages + English). The paper presents the tool functionality, the architecture, the digital library and provide some information about the technology involved in the fields of automatic speech recognition, statistical machine translation, text-to-speech synthesis and information retrieval. Each technology has been adapted to the purposes of the presented tool as well as to interact with the rest of the technologies involved.

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them.