Modeling Sub-Band Information Through Discrete Wavelet Transform to Improve Intelligibility Assessment of Dysarthric Speech

The speech signal within a sub-band varies at a fine level depending on the type, and level of dysarthria. The Mel-frequency filterbank used in the computation process of cepstral coefficients smoothed out this fine level information in the higher frequency regions due to the larger bandwidth of filters. To capture the sub-band information, in this paper, four-level discrete wavelet transform (DWT) decomposition is firstly performed to decompose the input speech signal into approximation and detail coefficients, respectively, at each level. For a particular input speech signal, five speech signals representing different sub-bands are then reconstructed using inverse DWT (IDWT). The log filterbank energies are computed by analyzing the short-term discrete Fourier transform magnitude spectra of each reconstructed speech using a 30-channel Mel-filterbank. For each analysis frame, the log filterbank energies obtained across all reconstructed speech signals are pooled together, and discrete cosine transform is performed to represent the cepstral feature, here termed as discrete wavelet transform reconstructed (DWTR)- Mel frequency cepstral coefficient (MFCC). The i-vector based dysarthric level assessment system developed on the universal access speech corpus shows that the proposed DTWRMFCC feature outperforms the conventional MFCC and several other cepstral features reported for a similar task. The usages of DWTR- MFCC improve the detection accuracy rate (DAR) of the dysarthric level assessment system in the text and the speaker-independent test case to 60.094 % from 56.646 % MFCC baseline. Further analysis of the confusion matrices shows that confusion among different dysarthric classes is quite different for MFCC and DWTR-MFCC features. Motivated by this observation, a two-stage classification approach employing discriminating power of both kinds of features is proposed to improve the overall performance of the developed dysarthric level assessment system. The two-stage classification scheme further improves the DAR to 65.813 % in the text and speaker- independent test case

DeepVOX: Discovering Features from Raw Audio for Speaker Recognition in Degraded Audio Signals

Automatic speaker recognition algorithms typically use pre-defined filterbanks, such as Mel-Frequency and Gammatone filterbanks, for characterizing speech audio. The design of these filterbanks is based on domain-knowledge and limited empirical observations. The resultant features, therefore, may not generalize well to different types of audio degradation. In this work, we propose a deep learning-based technique to induce the filterbank design from vast amounts of speech audio. The purpose of such a filterbank is to extract features robust to degradations in the input audio. To this effect, a 1D convolutional neural network is designed to learn a time-domain filterbank called DeepVOX directly from raw speech audio. Secondly, an adaptive triplet mining technique is developed to efficiently mine the data samples best suited to train the filterbank. Thirdly, a detailed ablation study of the DeepVOX filterbanks reveals the presence of both vocal source and vocal tract characteristics in the extracted features. Experimental results on VOXCeleb2, NIST SRE 2008 and 2010, and Fisher speech datasets demonstrate the efficacy of the DeepVOX features across a variety of audio degradations, multi-lingual speech data, and varying-duration speech audio. The DeepVOX features also improve the performance of existing speaker recognition algorithms, such as the xVector-PLDA and the iVector-PLDA

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Stress and emotion recognition in natural speech in the work and family environments

The speech stress and emotion recognition and classification technology has a potential to provide significant benefits to the national and international industry and society in general. The accuracy of an automatic emotion speech and emotion recognition relays heavily on the discrimination power of the characteristic features. This work introduced and examined a number of new linear and nonlinear feature extraction methods for an automatic detection of stress and emotion in speech. The proposed linear feature extraction methods included features derived from the speech spectrograms (SS-CB/BARK/ERB-AE, SS-AF-CB/BARK/ERB-AE, SS-LGF-OFS, SS-ALGF-OFS, SS-SP-ALGF-OFS and SS-sigma-pi), wavelet packets (WP-ALGF-OFS) and the empirical mode decomposition (EMD-AER). The proposed nonlinear feature extraction methods were based on the results of recent laryngological studies and nonlinear modelling of the phonation process. The proposed nonlinear features included the area under the TEO autocorrelation envelope based on different spectral decompositions (TEO-DWT, TEO-WP, TEO-PWP-S and TEO-PWP-G), as well as features representing spectral energy distribution of speech (AUSEES) and glottal waveform (AUSEEG). The proposed features were compared with features based on the classical linear model of speech production including F0, formants, MFCC and glottal time/frequency parameters. Two classifiers GMM and KNN were tested for consistency. The experiments used speech under actual stress from the SUSAS database (7 speakers; 3 female and 4 male) and speech with five naturally expressed emotions (neutral, anger, anxious, dysphoric and happy) from the ORI corpora (71 speakers; 27 female and 44 male). The nonlinear features clearly outperformed all the linear features. The classification results demonstrated consistency with the nonlinear model of the phonation process indicating that the harmonic structure and the spectral distribution of the glottal energy provide the most important cues for stress and emotion recognition in speech. The study also investigated if the automatic emotion recognition can determine differences in emotion expression between parents of depressed adolescents and parents of non-depressed adolescents. It was also investigated if there are differences in emotion expression between mothers and fathers in general. The experiment results indicated that parents of depressed adolescent produce stronger more exaggerated expressions of affect than parents of non-depressed children. And females in general provide easier to discriminate (more exaggerated) expressions of affect than males

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Perkeptuaalinen spektrisovitus glottisherätevokoodatussa tilastollisessa parametrisessa puhesynteesissä käyttäen mel-suodinpankkia

This thesis presents a novel perceptual spectral matching technique for parametric statistical speech synthesis with glottal vocoding. The proposed method utilizes a perceptual matching criterion based on mel-scale filterbanks. The background section discusses the physiology and modelling of human speech production and perception, necessary for speech synthesis and perceptual spectral matching. Additionally, the working principles of statistical parametric speech synthesis and the baseline glottal source excited vocoder are described. The proposed method is evaluated by comparing it to the baseline method first by an objective measure based on the mel-cepstral distance, and second by a subjective listening test. The novel method was found to give comparable performance to the baseline spectral matching method of the glottal vocoder.Tämä työ esittää uuden perkeptuaalisen spektrisovitustekniikan glottisvokoodattua tilastollista parametristä puhesynteesiä varten. Ehdotettu menetelmä käyttää mel-suodinpankkeihin perustuvaa perkeptuaalista sovituskriteeriä. Työn taustaosuus käsittelee ihmisen puheentuoton ja havaitsemisen fysiologiaa ja mallintamista tilastollisen parametrisen puhesynteesin ja perkeptuaalisen spektrisovituksen näkökulmasta. Lisäksi kuvataan tilastollisen parametrisen puhesynteesin ja perusmuotoisen glottisherätevokooderin toimintaperiaatteet. Uutta menetelmää arvioidaan vertaamalla sitä alkuperäiseen metodiin ensin käyttämällä mel-kepstrikertoimia käyttävää objektiivista etäisyysmittaa ja toiseksi käyttäen subjektiivisia kuuntelukokeita. Uuden metodin havaittiin olevan laadullisesti samalla tasolla alkuperäisen spektrisovitusmenetelmän kanssa

Machine learning for Arabic phonemes recognition using electrolarynx speech

Automatic speech recognition system is one of the essential ways of interaction with machines. Interests in speech based intelligent systems have grown in the past few decades. Therefore, there is a need to develop more efficient methods for human speech recognition to ensure the reliability of communication between individuals and machines. This paper is concerned with Arabic phoneme recognition of electrolarynx device. Electrolarynx is a device used by cancer patients having vocal laryngeal cords removed. Speech recognition here is considered to find the preferred machine learning model that can classify phonemes produced by electrolarynx device. The phonemes recognition employs different machine learning schemes, including convolutional neural network, recurrent neural network, artificial neural network (ANN), random forest, extreme gradient boosting (XGBoost), and long short-term memory. Modern standard Arabic is utilized for testing and training phases of the recognition system. The dataset covers both an ordinary speech and electrolarynx device speech recorded by the same person. Mel frequency cepstral coefficients are considered as speech features. The results show that the ANN machine learning method outperformed other methods with an accuracy rate of 75%, a precision value of 77%, and a phoneme error rate (PER) of 21.85%

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies