Apprentissage automatique pour le codage cognitif de la parole

Depuis les années 80, les codecs vocaux reposent sur des stratégies de codage à court terme qui fonctionnent au niveau de la sous-trame ou de la trame (généralement 5 à 20 ms). Les chercheurs ont essentiellement ajusté et combiné un nombre limité de technologies disponibles (transformation, prédiction linéaire, quantification) et de stratégies (suivi de forme d'onde, mise en forme du bruit) pour construire des architectures de codage de plus en plus complexes. Dans cette thèse, plutôt que de s'appuyer sur des stratégies de codage à court terme, nous développons un cadre alternatif pour la compression de la parole en codant les attributs de la parole qui sont des caractéristiques perceptuellement importantes des signaux vocaux. Afin d'atteindre cet objectif, nous résolvons trois problèmes de complexité croissante, à savoir la classification, la prédiction et l'apprentissage des représentations. La classification est un élément courant dans les conceptions de codecs modernes. Dans un premier temps, nous concevons un classifieur pour identifier les émotions, qui sont parmi les attributs à long terme les plus complexes de la parole. Dans une deuxième étape, nous concevons un prédicteur d'échantillon de parole, qui est un autre élément commun dans les conceptions de codecs modernes, pour mettre en évidence les avantages du traitement du signal de parole à long terme et non linéaire. Ensuite, nous explorons les variables latentes, un espace de représentations de la parole, pour coder les attributs de la parole à court et à long terme. Enfin, nous proposons un réseau décodeur pour synthétiser les signaux de parole à partir de ces représentations, ce qui constitue notre dernière étape vers la construction d'une méthode complète de compression de la parole basée sur l'apprentissage automatique de bout en bout. Bien que chaque étape de développement proposée dans cette thèse puisse faire partie d'un codec à elle seule, chaque étape fournit également des informations et une base pour la prochaine étape de développement jusqu'à ce qu'un codec entièrement basé sur l'apprentissage automatique soit atteint. Les deux premières étapes, la classification et la prédiction, fournissent de nouveaux outils qui pourraient remplacer et améliorer des éléments des codecs existants. Dans la première étape, nous utilisons une combinaison de modèle source-filtre et de machine à état liquide (LSM), pour démontrer que les caractéristiques liées aux émotions peuvent être facilement extraites et classées à l'aide d'un simple classificateur. Dans la deuxième étape, un seul réseau de bout en bout utilisant une longue mémoire à court terme (LSTM) est utilisé pour produire des trames vocales avec une qualité subjective élevée pour les applications de masquage de perte de paquets (PLC). Dans les dernières étapes, nous nous appuyons sur les résultats des étapes précédentes pour concevoir un codec entièrement basé sur l'apprentissage automatique. un réseau d'encodage, formulé à l'aide d'un réseau neuronal profond (DNN) et entraîné sur plusieurs bases de données publiques, extrait et encode les représentations de la parole en utilisant la prédiction dans un espace latent. Une approche d'apprentissage non supervisé basée sur plusieurs principes de cognition est proposée pour extraire des représentations à partir de trames de parole courtes et longues en utilisant l'information mutuelle et la perte contrastive. La capacité de ces représentations apprises à capturer divers attributs de la parole à court et à long terme est démontrée. Enfin, une structure de décodage est proposée pour synthétiser des signaux de parole à partir de ces représentations. L'entraînement contradictoire est utilisé comme une approximation des mesures subjectives de la qualité de la parole afin de synthétiser des échantillons de parole à consonance naturelle. La haute qualité perceptuelle de la parole synthétisée ainsi obtenue prouve que les représentations extraites sont efficaces pour préserver toutes sortes d'attributs de la parole et donc qu'une méthode de compression complète est démontrée avec l'approche proposée.Abstract: Since the 80s, speech codecs have relied on short-term coding strategies that operate at the subframe or frame level (typically 5 to 20ms). Researchers essentially adjusted and combined a limited number of available technologies (transform, linear prediction, quantization) and strategies (waveform matching, noise shaping) to build increasingly complex coding architectures. In this thesis, rather than relying on short-term coding strategies, we develop an alternative framework for speech compression by encoding speech attributes that are perceptually important characteristics of speech signals. In order to achieve this objective, we solve three problems of increasing complexity, namely classification, prediction and representation learning. Classification is a common element in modern codec designs. In a first step, we design a classifier to identify emotions, which are among the most complex long-term speech attributes. In a second step, we design a speech sample predictor, which is another common element in modern codec designs, to highlight the benefits of long-term and non-linear speech signal processing. Then, we explore latent variables, a space of speech representations, to encode both short-term and long-term speech attributes. Lastly, we propose a decoder network to synthesize speech signals from these representations, which constitutes our final step towards building a complete, end-to-end machine-learning based speech compression method. The first two steps, classification and prediction, provide new tools that could replace and improve elements of existing codecs. In the first step, we use a combination of source-filter model and liquid state machine (LSM), to demonstrate that features related to emotions can be easily extracted and classified using a simple classifier. In the second step, a single end-to-end network using long short-term memory (LSTM) is shown to produce speech frames with high subjective quality for packet loss concealment (PLC) applications. In the last steps, we build upon the results of previous steps to design a fully machine learning-based codec. An encoder network, formulated using a deep neural network (DNN) and trained on multiple public databases, extracts and encodes speech representations using prediction in a latent space. An unsupervised learning approach based on several principles of cognition is proposed to extract representations from both short and long frames of data using mutual information and contrastive loss. The ability of these learned representations to capture various short- and long-term speech attributes is demonstrated. Finally, a decoder structure is proposed to synthesize speech signals from these representations. Adversarial training is used as an approximation to subjective speech quality measures in order to synthesize natural-sounding speech samples. The high perceptual quality of synthesized speech thus achieved proves that the extracted representations are efficient at preserving all sorts of speech attributes and therefore that a complete compression method is demonstrated with the proposed approach

Speech Development by Imitation

The Double Cone Model (DCM) is a model of how the brain transforms sensory input to motor commands through successive stages of data compression and expansion. We have tested a subset of the DCM on speech recognition, production and imitation. The experiments show that the DCM is a good candidate for an artificial speech processing system that can develop autonomously. We show that the DCM can learn a repertoire of speech sounds by listening to speech input. It is also able to link the individual elements of speech to sequences that can be recognized or reproduced, thus allowing the system to imitate spoken language

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

Telephone speech enhancement for the hearing impaired

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2009.Cataloged from PDF version of thesis report.Includes bibliographical references (page 48).Bony TasnimC. Z. MurshedB. Computer Science and Engineerin

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

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

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

The Effect Of Acoustic Variability On Automatic Speaker Recognition Systems

This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre-forensic use. The thesis begins with a literature review and explanation of relevant technical terms. Five categories of research experiments then examine ASR performance, reflective of conditions influencing speech quantity (inhibitors) and speech quality (contaminants), acknowledging quality often influences quantity. Experiments pertain to: net speech duration, signal to noise ratio (SNR), reverberation, frequency bandwidth and transcoding (codecs). The ASR system is placed under scrutiny with examination of settings and optimum conditions (e.g. matched/unmatched test audio and speaker models). Output is examined in relation to baseline performance and metrics assist in informing if ASRs should be applied to suboptimal audio recordings. Results indicate that modern ASRs are relatively resilient to low and moderate levels of the acoustic contaminants and inhibitors examined, whilst remaining sensitive to higher levels. The thesis provides discussion on issues such as the complexity and fragility of the speech signal path, speaker variability, difficulty in measuring conditions and mitigation (thresholds and settings). The application of ASRs to casework is discussed with recommendations, acknowledging the different modes of operation (e.g. investigative usage) and current UK limitations regarding presenting ASR output as evidence in criminal trials. In summary, and in the context of acoustic variability, the thesis recommends that ASRs could be applied to pre-forensic cases, accepting extraneous issues endure which require governance such as validation of method (ASR standardisation) and population data selection. However, ASRs remain unsuitable for broad forensic application with many acoustic conditions causing irrecoverable speech data loss contributing to high error rates