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Background:- An earlier article in this journal (Earle 2014) provided a mapping of primary science teacher assessment practices in England using teacher reflections written for Round 4 of the Primary Science Quality Mark (PSQM).\ud \ud Purpose:- In the intervening years, the National Curriculum and statutory assessment system of levels has been replaced with a system of age-related expectations and a call for schools to develop their own systems of assessment. The aim of this study is to find out whether schools have changed their practices and to consider the ongoing changes they are making to their systems of assessment.\ud \ud Sample:- The previous analysis of 91 Round 4 schools in March 2013 was compared to teacher reflections from 200 schools from across England in the more recent PSQM Round 13, June 2017.\ud \ud Design and methods:- Qualitative content analysis was used to code the reported assessment methods in PSQM Round 13. The frequency of assessment methods were compared to the Round 4 mapping. \ud \ud Results:- Reported practice in summative assessment in Round 13 included more ‘ongoing’ assessment information, which could potentially support a more valid sampling of the construct, but there was a lack of clarity regarding the use of data tracking systems. In the PSQM year, subject leaders described starting or developing the use of a range of published and school developed resources to support assessment practices in formative and summative assessment.\ud \ud Conclusion:- Changing statutory assessment processes instigates change in school practices, but without support for teacher assessment literacy, the implementation of new assessment systems may not lead to clear processes for assessment which support learning in primary science.

The MOS-based speech quality measurement methods allow us to determine generic encoder quality assessments on IP networks. However, they have several cautions to working within real-time, as they require the involvement of highly qualified experts. The paper considers alternative methods of quality assessment based on PESQ - PSQM algorithms, taking into account network factors such as delay, jitter, and packet loss during network congestion. Also, the paper shows the efficiency of using MSE calculation. Conducted an experimental study of the relationship between MSE and PESQ score for different speech signal encoders and performed conversion of MSE - MOS. The results are easily adaptable to new types of encoders and to assess the deterioration of voice signal transmission when exposed to a variety of network factors.

This letter presents a perceptually weighted analysis-by-synthesis vector quantization (VQ) algorithm for low bit rate MFCC codec. Different from conventional VQ of mel-frequency cepstral coefficients (MFCCs) vector, this algorithm uses an analysis-by-synthesis technique and aims to minimize the perceptually weighted spectral reconstruction distortion rather than the distortion of MFCCs vector itself. Also, to reduce the computational complexity, we propose a practical suboptimal codebook searching technique and embed it into the split and multistage VQ framework. Objective and subjective experimental results on Mandarin speech show that the proposed algorithm yields intelligible and natural sounding speech for speech coding at 600–2400 bit/s. Compared to current VQ in MFCC codec, the output speech quality is substantially improved in terms of frequency-weighted segmental SNR, short-time objective intelligibility score, perceptual evaluation of speech quality score, and mean opinion score.

During the last five decades, extensive researches have been carried out in the field of speech compression, which has resulted in various techniques for speech coding. Researchers have been in full swing for more efficient speech coding and their effort is still continuing in different parts of the world. In this paper we are proposing an alternative method for better speech coding. In the proposed technique we use discrete wavelet transform to decompose the signal and wavelet energy is used to differentiate between active voice region and silence region in the speech signal. Depending upon the region’s status the system, different thresholding strategies have been chosen which leads to a better compression without any loss of speech intelligibility. The proposed method is evaluated in terms of qualitative and quantitative parameters. In this paper we also propose an alternative parameter for MOS values which is here after known as System Recognition Rate.

In this paper, automatic assessment models are developed for two perceptual variables: speech intelligibility and voice quality. The models are developed and tested on a corpus of Dutch tracheoesophageal (TE) speakers. In this corpus, each speaker read a text passage of approximately 300 syllables and two speech therapists provided consensus scores for the two perceptual variables. Model accuracy and stability are investigated as a function of the amount of speech that is made available for speaker assessment (clinical setting). Five sets of automatically generated acoustic-phonetic speaker features are employed as model inputs. In Part I, models taking complete feature sets as inputs are compared to models taking only the features which are expected to have sufficient support in the speech available for assessment. In Part II, the impact of phonetic content and stimulus length on the computer-generated scores is investigated. Our general finding is that a text encompassing circa 100 syllables is long enough to achieve close to asymptotic accuracy.

This paper presents our study on speech recognition performance prediction. Our initial study shows that a combination of speech quality measures effectively improves correlation with Word Error Rate (WER) compared to each speech measure alone. In this paper we demonstrate a new combination of various types of speech quality measures shows more significantly improves correlation with WER compared to the speech measure combination of our initial study. In our study, SNR, PESQ, acoustic model score, and MFCC distance are used as the speech quality measures. This paper also presents our speech database verification system for speech recognition employing the speech measures. We develop a WER prediction system using Gaussian mixture model and the speech quality measures as a feature vector. The experimental results show the proposed system is highly effective at predicting WER in a low SNR condition of speech babble and car noise environments. 키워드 : 단어 오인식률, 상관 계수, 성능 예측, 음성 인식, 음성 인식 성능 지표

Postfiltering is a well known technique that helps increasing the quality of coded speech, enhancing speech intelligibility or alleviating the oversmoothing effect of statistical speech processing methods. This letter presents a new formulation of the radial cepstral postfiltering method that enables the application of different postfiltering factors to low and high frequencies. The transition between bands will be smooth and controllable through an adjustable cut-off frequency. The proposed algorithm can be implemented by means of a simple multiplicative matrix of which an analytical expression is derived. The new method provides a flexible framework to tackle issues related to the quality and intelligibility of synthetic speech.

An improved speech recognition algorithm has been developed with additional signal processing units to increase the number of comparative data parameters and to reduce the natural and spectral distortion of speech signals. Using a verified database an experimental study of the algorithm was conducted.

Three objective measures are proposed to separately evaluate the speech distortion, noise reduction and overall quality of the processed speech signal enhanced by single channel speech enhancement algorithms.The proposed measures are derived in both time and frequency domains.The high correlations between the measurement results of the proposed approaches and subjective ratings demonstrate the effectiveness of the proposed evaluation methodology.Some hints and links between signal parameters and perceptual judgements are revealed in our analysis on the evaluation process of the proposed objective measures with subjective ratings. Among all the existing objective measures, few are able to provide a clearly specific indication of speech distortion or noise reduction, which are the two key metrics to assess the performance of speech enhancement algorithms and evaluate the noise-suppressed speech quality. In this paper, new quantitative quality assessments are proposed to separately evaluate the capabilities of single channel speech enhancement algorithms in terms of maintaining the clean speech, noise reduction and overall performance. Based on these aspects, three evaluation results can be provided for any one test speech signal by analyzing the residual signal which is the difference between the clean speech and the processed speech. Several common speech enhancement algorithms are compared by these objective measures as well as subjective listening tests. High correlations between the scores of the objective measures and subjective ratings clearly show the effectiveness of the proposed evaluation methodologies on the different speech enhancement algorithms.

Streaming services seek to optimise their use of bandwidth across audio and visual channels to maximise the quality of experience for users. This letter evaluates whether objective quality metrics can predict the audio quality for music encoded at low bitrates by comparing objective predictions with results from listener tests. Three objective metrics were benchmarked: PEAQ, POLQA, and VISQOLAudio. The results demonstrate objective metrics designed for speech quality assessment have a strong potential for quality assessment of low bitrate audio codecs.

This paper deals with the impact of transcod- ing on the speech quality. We have focused mainly on the transcoding between codecs without the negative in- fluence of the network parameters such as packet loss and delay. It has ensured objective and repeatable re- sults from our measurement. The measurement was performed on the Transcoding Measuring System devel- oped especially for this purpose. The system is based on the open source projects and is useful as a design tool for VoIP system administrators. The paper com- pares the most used codecs from the transcoding per- spective. The multiple transcoding between G711, GSM and G729 codecs were performed and the speech qual- ity of these calls was evaluated. The speech quality was measured by Perceptual Evaluation of Speech Quality method, which provides results in Mean Opinion Score used to describe the speech quality on a scale from 1 to 5. The obtained results indicate periodical speech quality degradation on every transcoding between two codecs.

The use of single time-instance features, where entire speech utterance is used for feature computation, is not accurate and adequate in capturing the time localized information of short-time transient distortions and their distinction from plosive sounds of speech, particularly degraded by impulsive noise. Hence, the importance of estimating features at multiple time-instances is sought. In this, only active speech segments of degraded speech are used for features computation at multiple time-instances on per frame basis. Here, active speech means both voiced and unvoiced frames except silence. The features of different combinations of multiple contiguous active speech segments are computed and called multiple time-instances features. The joint GMM training has been done using these features along with the subjective MOS of the corresponding speech utterance to obtain the parameters of GMM. These parameters of GMM and multiple time-instances features of test speech are used to compute the objective MOS values of different combinations of multiple contiguous active speech segments. The overall objective MOS of the test speech utterance is obtained by assigning equal weight to the objective MOS values of the different combinations of multiple contiguous active speech segments. This algorithm outperforms the Recommendation ITU-T P.563 and recently published algorithms.

The main aim of this study was to prepare a new speech database for the purpose of unit selection speech synthesis. The object was to design a database with improved parameters compared with the existing database [1], making use of the theses proved in studies [2]-[4]. The quality of the corpus, a selection of the suitable speaker, and the quality of the speech database are all crucially important for the quality of synthesized speech. The considerably larger text corpora used in the study as well as the broader multiple balancing of the database yielded a greater number of varied acoustic units. For the purpose of the recording, one voice talent was selected from among a group of 30 professional speakers. The next stage involved database segmentation. The resultant database was then verified with a prototype speech synthesizer. The quality of the synthetic speech was compared to that of synthetic speech obtained in other Polish unit selection speech synthesis systems. Consequently, the end result proved to be better than the one obtained in the previous study [4]. The database had been supplemented and extended, significantly enhancing the quality of synthesized speech.

This paper presents a very low bit-rate speech codec based on the long-term Harmonic plus Noise Model (LT-HNM). The HNM is known to be efficient in terms of speech signal representation, thanks to the use of natural parameters: fundamental and voicing cut-off frequencies, harmonics and noise frequencies. Besides, the long-term modeling is particularly efficient in reducing the data size of the model parameters. In this paper we combine both approaches, long-term modeling and HNM, to develop a very low bit-rate coder for narrowband speech. The obtained bit-rates are as low as 2.3 kbps with objective listening quality (perceptual evaluation of speech quality PESQ) of 2.3.

Mobile devices are widely used today for speech communication. The environments in which these devices are used are widely varied and often the level of background noise in the speaker's environment can be significant. The purpose of speech enhancement is to reduce the level of background noise, ideally to such a level that it is not noticed by the listener. While speech enhancement algorithms can significantly reduce the noise level in a speech signal, improving speech quality, it is widely recognized that enhancement algorithms can have a negative impact on speech intelligibility. This paper compares the effect of three different speech enhancement algorithms on the intelligibility and the quality of speech. This work is the initial phase of an investigation into mitigating the impact of speech enhancement algorithms on speech intelligibility. The speech enhancement algorithms evaluated each use different approaches for noise reduction, namely, a statistical model-based algorithm, a noise estimation algorithm and a wavelet packet decomposition-based algorithm. Two objective speech intelligibility measurements and three objective speech quality measurements are used to assess the performance of the enhancement algorithms. The results of the experiments show that all the speech enhancement algorithms in this study have a negative impact on speech intelligibility to varying degrees.

This paper proposes a novel non-intrusive auditory perception-based approach for disordered speech quality estimation. An adaptive time-frequency algorithm, viz. the Matching Pursuit (MP) algorithm, is used to generate a reference signal from the disordered speech signal. Both the generated reference signal and the original degraded signal are given to the International Telecommunication Union (ITU)-standardized Perceptual Evaluation of Speech Quality (PESQ) estimator to obtain a quality score. Our approach is tested on two different databases consisting of tracheoesophageal speech samples. Results show that our method performs significantly better than the conventional acoustical measures of disordered speech quality.

In this work, we study the effect of codec induced distortion on the speech recognition performance in the TIMIT corpus using eleven codecs and five acoustic modeling techniques (AMTs) including several state-of-the-art methods. This study is performed in a single round of encoding-decoding and various tandem scenarios. Experiments from the single encoding-decoding case reveal that the acoustic models from G.711A, a narrowband high bit rate codec yields lower phone error rate (PER) compared to low bit rate codecs for most AMTs. It is observed that among the eleven codecs based acoustic models, G.711A, G.728, G.729B, AMR-WB and G.729A codecs consistently result in the least five PERs across AMTs. It is found that the model trained on ‘clean’ speech data (PCM) performs poorly in three of the five AMTs compared to these five codec based acoustic models. These five models are then used in six different tandem scenarios comprising three unseen codecs. Similar to the single round of encoding-decoding case, the PER for each of the tandem scenarios turns out to be the lowest consistently for all AMTs when the acoustic model from the G.711A codec is used. However, when the acoustic model is trained with mixed speech data from all tandem scenarios, the PER is found to perform better than the matched condition in the case of four out of five AMTs.

When speaking devices such as smartphones, tablet-PCs, or GPS systems are used in noisy outdoor environments, the intelligibility of speech significantly drops. This is even more pronounced when synthetic speech is used. This article describes how a statistical parametric speech synthesis system trained on an ordinary synthesis database can be designed to generate highly intelligible speech, even at very low signal-to-noise ratios. By using a simple and flexible vocoder based on a full-band harmonic model, the proposed system applies deterministic noise-independent modifications at several levels: speaking rate, average fundamental frequency level and range, energy contour over time, formant sharpness, and intensity of specific spectral bands. The degree of intelligibility achieved by the system has been evaluated by means of a large-scale subjective test, the results of which show that the suggested approach clearly outperforms a reference state-of-the-art TTS system and also unmodified natural speech in some conditions. In comparison with alternative systems evaluated in the same framework, the proposed one exhibits the best performance in the scenarios with lowest signal-to-noise ratio. Finally, the impact of the suggested modifications on naturalness, quality and similarity to the original natural voice is quantified by means of a subjective test.

Speech coding is the art of creating a minimally redundant representation of the speech signal that can be efficiently transmitted or stored in digital media and decoding the signal with the best possible perceptual Quality. The speech transmission in wireless networks is associated with the reduction of extra information present in signal in such a way to preserve the quality and intelligibility of speech. It is known that the lower the bit rate the lesser the quality of the reconstructed speech however there is a constant quest to achieve a better speech quality at lower bit-rates.This paper presents performance analysis for the quality of advanced hybrid speech coding techniques in Time domain, Spectral domain and perceptual domain. These analyses are implemented on three different algorithms of advanced hybrid speech coding techniques such as CELP, G729 Annex A, G723.1 to assess the quality performance for English female speaker, English male speaker and Arabic female speaker by using Mat lab simulation program. Our evaluation criterion implemented includes the following tests: Signal to Noise Ratio (SNR), Segmental Signal to Noise Ratio (SNRseg), The Log-Likelihood Ratio (LLR), The Weighted Spectral Slope (WSS), Absolute Error,Perceptual Evaluation of Speech Quality (PESQ),NONE">Rating of speechdistortion, rating of background noise and the predicted rating of overall quality.

Ultra-low-bit-rate speech coding algorithm was in great demand for many fields such as underwater speech communications. Underwater speech communication for middle-long distance has the characteristics of narrow bandwidth as well as low transmission rate, which makes the underwater speech communication much difficult. Ultra-low-bit-rate speech coding algorithm plays an important role on this occasion. More over, it will be more flexible for the underwater speech communication system if the speech coding algorithm has an embedded structure. The paper introduced the principle of an embedded speech coding algorithm with dual rates at both 300bps and 400bps based on the enhanced mixed excitation linear prediction model. The results show that this embedded ultra-low-bit-rate speech coding algorithm has satisfactory quality under both DRT and MOS test.

Audio communications in IP based networks have been revolutionized by the introduction of VoIP applications. High cost-efficiency has made VoIP to be the communication means in today’s world; and this trend is anticipated to be continued on an ongoing basis. The performance of VoIP significantly depends on the efficiency of the audio codecs used in any communication scenario which make the study on the performance issues of audio codecs in VoIP applications worth investigating. IPv6 is the new version of IP, which will gradually replace the current IPv4 as the transition from IPv4 to IPv6 is already in place. This demands the scrutiny of the audio codecs being used in IPv4 to be tested for their compatibility in IPv6 in terms of desired performance. This paper presents the study on the performance of selected audio codecs that are widely used in VoIPv4. G.711, G.729A and G.723.1 codecs were chosen for the study in VoIPv6 based scenarios presented in this paper. The selected audio codecs were applied in IPv6 based voice communication network scenarios to determine their performance efficiency by observing various QoS parameters. The study was done by means of simulation using OPNET.

Speech-in-noise tests commonly use short, discrete sentences as representative samples of everyday speech. These tests cannot, however, fully represent the added demands of understanding ongoing, linguistically complex speech. Using a new monitoring method to measure the intelligibility of continuous speech and a standard trial-by-trial, speech-in-noise test the effects of target duration and linguistic complexity were examined. For a group of older hearing-impaired listeners, significantly higher speech reception thresholds were found for continuous, complex speech targets than for syntactically simple sentences. The results highlight the need to sample speech intelligibility in a variety of everyday speech-in-noise scenarios.

SUMMARY In this paper, we study an objective quality measure that approximates the subjective mean opinion score (MOS) for bandwidthextended wideband speech with respect to narrowband speech. Bandwidthextended speech should be widely evaluated by a subjective quality assessment such as MOS. However, such subjective quality assessments are expensive and time-consuming. This paper proposes a new objective quality measure that combines the perceptual evaluation of speech quality (PESQ) and spectral-distortion. We evaluated the correlation between our proposed scheme and MOS using AMR and AMR-WB speech codecs. The coefficient of correlation between the proposed scheme and the MOS value was found to be 0.973. We concluded that the proposed scheme is a valid and effective objective quality measure.

Despite the established importance of temporal fine-structure (TFS) on speech perception in noise, existing speech transmission metrics use primarily envelope information to model speech intelligibility variance. This study proposes a new physical metric for predicting speech intelligibility using information obtained from the Hilbert-derived TFS waveform. It is found that by making explicit use of coherence information contained in the complex spectra of the Hilbert-derived TFS waveforms of the clean and corrupted speech signals, and assessing the extent to which the coherence in the Hilbert fine structure is affected following the linear or non-linear processing (e.g., noise distortion, speech enhancement, etc.) of the stimulus, the predictive power of the intelligibility measure can be significantly improved for noise-distorted and noise-suppressed speech signals. When evaluated with speech recognition scores obtained with normal-hearing listeners, including a total of sixty-four noise-suppressed conditions with nonlinear distortions and eight noisy conditions without subsequent noise reduction, the proposed TFS-based measure was found to predict speech intelligibility better than most envelope- and coherence-based measures. High correlation was maintained for all types of maskers tested, with a maximum correlation of r=0.95 achieved in car and street noise conditions.

Here we describe a speech-synthesis method using templates that can generate recording-sentence sets for speech databases and produce natural sounding synthesized speech. Applying this method to the Japan Broadcasting Corporation (NHK) weather report radio program reduced the size of the recording-sentence set required to just a fraction of that needed by a comparable method. After integrating the recording voice of the generated recording-sentence set into the speech database, speech was produced by a voice synthesizer using templates. In a paired-comparison test, 66% of the speech samples synthesized by our system using templates were preferred to those produced by a conventional voice synthesizer. In an evaluation test using a five-point mean opinion score (MOS) scale, the speech samples synthesized by our system scored 4.97, whereas the maximum score for commercially available voice synthesizers was 3.09. In addition, we developed an automatic broadcast system for the weather report program using the speech-synthesis method and speech-rate converter. The system was evaluated using real weather data for more than 1 year, and exhibited sufficient stability and synthesized speech quality for broadcast purposes.

This paper provides an overview of instrumental models for predicting the quality of speech signals. On the basis of perceptual and cognitive characteristics of the human auditory system which are relevant for quality judgment, approaches are presented which aim at predicting overall quality, intelligibility, or other quality dimensions from measurable parameters or signal characteristics. The approaches are discussed with respect to their underlying principles, showing that perception modeling can significantly improve prediction accuracy. Application examples are presented which make use of these algorithms for offline or online prediction, adaptation, or intelligibility improvement.

This paper evaluates the various families of wavelet transform and their performance on compression of speech signal to be used for VoIP communication. The prime focus in using wavelet-based speech coder is the selection of a best possible wavelet function for signal processing. Haar wavelets, different orders of Daubechies wavelets, Coiflet wavelets, and Discrete Meyer wavelets have been studied in this paper for speech coding. Speech codecs based on these wavelets are simulated, and the performance of speech compression has been compared by statistical analysis for subjective and objective testing.

This paper presents a psychoacoustic-based analysis-by-synthesis approach for compressing navigable speech sources. The approach targets multi-party teleconferencing applications, where selective reproduction of individual speech sources is desired. Based on exploiting sparsity of speech in the perceptual time-frequency domain, multiple speech signals are encoded into one mono mixture signal, which can be further compressed using a standard speech codec. Using side information indicating the active speech source for each time frequency instant enables flexible decoding and reproduction. Objective results highlight the importance of considering perception when exploiting the sparse nature of speech in the time-frequency domain. Results show that this sparsity, as measured by the preserved energy level of perceptually important time-frequency components extracted from mixtures of speech signals, is similar in both anechoic and reverberant environments. The proposed approach is applied to a series of simulated and real reverberant speech recordings, where the resulting speech mixtures are compressed using a standard speech codec operating at 32 kbps. The perceptual quality, as judged both by objective and subjective evaluations, outperforms a simple sparsity approach that does not consider perception as well as the approach that encodes each source separately. While the perceptual quality of individual speech sources is maintained, subjective tests also confirm the approach maintains the perceptual quality of the spatialized speech scene.

It is known that applying a time-frequency binary mask to very noisy speech can improve its intelligibility but results in poor perceptual quality. In this paper we propose a new approach to applying a binary mask that combines the intelligibility gains of conventional binary masking with the perceptual quality gains of a classical speech enhancer. The binary mask is not applied directly as a time-frequency gain as in most previous studies. Instead, the mask is used to supply prior information to a classical speech enhancer about the probability of speech presence in different time-frequency regions. Using an oracle ideal binary mask, we show that the proposed method results in a higher predicted quality than other methods of applying a binary mask whilst preserving the improvements in predicted intelligibility.

Narrow-band speech coding techniques were previously found to reduce the accuracy of automatic Speech Emotion Recognition (SER), as well as speech and speaker recognition rates. Artificial Bandwidth Extension (ABE) based on spectral folding and spectral envelope estimation has been applied to compressed narrowband speech to test if an improvement in SER can be achieved. The modelling and classification of speech was performed with a benchmark approach based on the GMM classifier and a set of speech acoustic parameters including MFCCs, TEO and glottal parameters. The tests used the Berlin Emotional Speech data base. In general, ABE led to an improvement of SER accuracy; however the amount of improvement varied between different features, genders, and speech compression rates. In all cases, SER accuracy with ABE was at least 10% lower than for uncompressed speech.

In this work, multiple time-scale estimates of auditory features have been introduced to capture the effect of short time-transients additive noise present over some specific active regions in a speech utterance and these multiple time-scale auditory features have been used for non-intrusive speech quality measurement. The use of single time-scale auditory features is not accurate in capturing the time localized information of short-time transient distortions and their distinction from plosive sounds of speech. Hence, the importance of estimating auditory features at multiple time-scales that is relevant for objective non-intrusive speech quality estimation. The different active speech segments obtained from voice activity detection (VAD) algorithm of speech utterance are combined across the segments in increasing number of active speech segments till all the segments of complete speech utterance are accounted for. The Lyon's auditory features of the combination of active speech segments are computed on frame by frame basis. The mean, variance, skewness and kurtosis over the frames of the auditory features are computed and concatenated to obtain multiple time-scale estimates of auditory features for the different combination active speech segments. These multiple time-scale auditory features are probabilistically modeled using Gaussian Mixture Model (GMM) to map into mean opinion score (MOS) value for each combination of active speech segments. The overall objective MOS of the degraded speech is obtained by taking average of MOS values of the combination of active speech segments. A detailed result comparison has been done with the ITU-T Recommendation P.563 for telephone band speech.

To ensure a satisfactory QoE (Quality of Experience), it is essential to establish a method that can be used to efficiently investigate recognition performance for spontaneous speech. By using this method, it is allowed to monitor the recognition performance in providing speech recognition services. It can be also used as a reliability measure in speech dialogue systems. Previously, methods for estimating the performance of noisy speech recognition based on spectral distortion measures have been proposed. Although they give an estimate of recognition performance without actually performing speech recognition, the methods cannot be applied to spontaneous speech because they require the reference speech to obtain the distortion values. To solve this problem, we propose a novel method for estimating the recognition performance of spontaneous speech with various speaking styles. The main feature is to use non-reference acoustic features that do not require the reference speech. The proposed method extracts non-reference features by openSMILE (open-Source Media Interpretation by Large feature-space Extraction) and then estimates the recognition performance by using SVR (Support Vector Regression). We confirmed the effectiveness of the proposed method by experiments using spontaneous speech data from the OGVC (On-line Gaming Voice Chat) corpus.

Majority of Speech Emotion Recognition results refer to full-band uncompressed speech signals. Potential applications of SER on various types of speech platforms pose important questions about potential effects of bandwidth limitations and compression techniques used by speech communication systems on the accuracy of SER. The current study provides answers to these questions based on SER experiments with a band-limited speech as well as compressed speech. Compression techniques included AMR, AMR-WB, AMR-WB+ and mp3 methods. The modelling and classification of speech emotions was achieved using a benchmark approach based on the GMM classifier and speech features including MFCCs, TEO and glottal time and frequency domain parameters. The tests used the Berlin Emotional Speech database with speech signals sampled at 16 kHz. The results indicated that the low frequency components (0–1 kHz) of speech as well as, the high frequency components (above 4 kHz) play an important role in SER. The mp3 compression worked better with the MFCC features than with the TEO and glottal parameters. The AMR-WB and AMR-WB+ outperformed the AMR.

The accuracy of the reproduction of emotions by speech coders has only recently been identified as a relevant issue. Several published studies have shown that speech compression reduces the accuracy of emotions classification. These studies, however, were all conducted using objective evaluation methods that involve an automatic classifier. The only definitive way to prove or disprove that the emotional content of a speech signal is degraded by compression operations is by testing it with human subjects. This paper proposes a subjective evaluation method and applies it to emotional speech coded by the AMR-WB speech coder at 6.6 and 12.65 kbps. The results confirm that there is a significant degradation in the perception of emotions by human listeners at both bit rates. The proposed evaluation method, and the insight provided by the results, could be useful in developing new speech coders that better preserve the emotional content of speech signals.

The modelling of the network effects on the quality of speech in the Voice over IP networks is the main focus of this paper. The main purpose of the ideas presented here is to achieve high-precision estimation of the speech quality in the environments where the classical approaches of speech quality determination fail. To achieve this high precision a modular neural network model is used to map the effects of a packet loss on the speech quality based on the PESQ reference. To incorporate the temporal effects E-model is partially utilized as well. This way a universal tool capable of harnessing the information about the speech quality for stress testing and monitoring of the local infrastructure has been developed enabling the telephony infrastructure administrators to evaluate the performance and stability of the systems in their hands. Moreover, a high-performance simulation environment has been developed as well to ensure sufficient amount of measurement data for the statistical analysis.

A low-power and high-speed speech enhancement processor for speech enhancement of noisy inputs is proposed to realize the robust speech recognition in smart glasses. It has 3 key schemes: multi-modal speech selection, look-up table based non-linear approximation circuits, and speech detection controlled dynamic clock gating. The multi-modal speech selection scheme uses three parameters to enhance the limited accuracy of the previous uni-modal user speech selection up to 98.1%. The non-linear function approximation circuit accelerates the throughput of the speech enhancement by 10.7×. The speech detection controlled clock gating reduces the redundant power consumption by 51% when there is no user voice. The proposed speech enhancement processor achieves 1.6Msamples/s throughput and 8.3mW average power consumption with the 98.1% true positive rate of speech selection in 65nm CMOS process.

Enhancement of speech in non stationary noise environments has been a major research topic due to its wide range of applications, e.g., in hearing aids. This paper proposes a method for performing dual channel speech enhancement. The proposed method involves the estimation of speech and noise short term predictor (STP) parameters using a codebook based approach, when we have access to binaural noisy signals. The estimated STP parameters are subsequently used for enhancement in a dual channel scenario. Objective measures indicate, that the proposed method is able to improve the speech intelligibility and quality.

With the development of network and speech coding, various applications of transmitting voice over internet protocol (VoIP) come into our lives. Speech reconstruction is required, as the packet loss caused by the real-time constraints transmission protocols results in the loss of speech segments. The paper proposes a novel approach to addressing speech reconstruction problem utilizing a high-order sparse linear predictor. This predictor is able to maintain the high sparsity level involved by the classical speech model as much as possible. Then we adopt differential evolution algorithm, which is arguably one of the most powerful stochastic real-parameter optimization algorithms, to predict the sparse coefficients and missing speech segments simultaneously. Experiments show the superior performance of this new approach in both signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ).

In this paper, the development of a speech processing pipeline on smartphones for hearing aid devices (HADs) is presented. This pipeline is used for noise suppression and speech enhancement (SE) to improve speech quality and intelligibility. The proposed method is implemented to run in real-time on Android smartphones. The results of the testing conducted indicate that the proposed method suppresses the noise and improves the perceptual quality of speech in terms of three objective measures of perceptual evaluation of speech quality (PESQ), noise attenuation level (NAL), and the coherent speech intelligibility index (CSII).

Low bit rate speech coding is important to speech communications over band-limited or harsh channels. In this paper, based on the mixed excitation linear prediction (MELP) model, we propose a high-quality 400bps low bit rate speech coding algorithm which introduces multi-frame joint vector quantization, adaptive spectral enhancement and multi-band sinusoidal mixed excitation. Efficient parameter quantization schemes are employed on the basis of the super-frame structure. It is verified that the synthesized speech has fairly high intelligibility and naturalness, and the mean opinion score (MOS) is about 2.52.

A qualified speech communication is quite important in modern communication. Thus, the implementation of security algorithms is also very important to achieve real-time applications. In this paper, we design a robust and full-duplex real-time speech communication system based on Texas Instrument's 32-bit floating point DSP TMS320C6748. We use ITU-T G.723.1 as the audio codec in this system. According to the hardware of the system and also to meet the requirement of high-quality and real-time communication, several methods are introduced in this paper to optimize the algorithm. Through the optimization, time spending in compression and decompression reduce from more than 1000ms to about 10ms which guarantees a real-time communication. Our designed system has been successfully used for the speech communication of railway system.

A voice activity detector (VAD) is a device, which analyses a speech signal and generates the signal corresponding to the period containing only noise. In the present work is offered VAD, which is intended for voice coding algorithms which primarily require correct pauses detection in human speech.

Redundant information of low-bit rate speech is extremely small, thus it's very difficult to implement large capacity steganography on the low-bit rate speech. Based on multiple vector quantization characteristics of the Line Spectrum Pair (LSP) of the speech codec, this paper proposes a steganography scheme using a 3D-sudoku matrix to enlarge capacity and improve quality of speech. A cyclically moving algorithm to construct 3D-Sudoku matrix for steganography is proposed in this paper, as well as an embedding and an extracting algorithm of steganography based on 3D-Sudoku matrix in low-bit rate speech codec. Theoretical analysis is provided to demonstrate that the concealment and the hidden capacity are greatly improved with the proposed scheme. Experimental results show the hidden capacity is raised to 200bps in ITU-T G.723.1 codec. Moreover, the quality of steganography speech in Perceptual Evaluation of Speech Quality (PESQ) reduces no more than 4%, indicating little impact on the quality of speech.