Glottal-synchronous speech processing

Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity of voiced speech is exploited. Traditionally, speech processing involves segmenting and processing short speech frames of predefined length; this may fail to exploit the inherent periodic structure of voiced speech which glottal-synchronous speech frames have the potential to harness. Glottal-synchronous frames are often derived from the glottal closure instants (GCIs) and glottal opening instants (GOIs). The SIGMA algorithm was developed for the detection of GCIs and GOIs from the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and GOI detection from speech signals, the YAGA algorithm provides a measured accuracy of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to reverberation than single-channel algorithms. The GCIs are applied to real-world applications including speech dereverberation, where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance of voicing detection in glottal-synchronous algorithms is demonstrated by subjective testing. The GCIs are further exploited in a new area of data-driven speech modelling, providing new insights into speech production and a set of tools to aid deployment into real-world applications. The technique is shown to be applicable in areas of speech coding, identification and artificial bandwidth extension of telephone speec

Models for learning reverberant environments

Reverberation is present in all real life enclosures. From our workplaces to our homes and even in places designed as auditoria, such as concert halls and theatres. We have learned to understand speech in the presence of reverberation and also to use it for aesthetics in music. This thesis investigates novel ways enabling machines to learn the properties of reverberant acoustic environments. Training machines to classify rooms based on the effect of reverberation requires the use of data recorded in the room. The typical data for such measurements is the Acoustic Impulse Response (AIR) between the speaker and the receiver as a Finite Impulse Response (FIR) filter. Its representation however is high-dimensional and the measurements are small in number, which limits the design and performance of deep learning algorithms. Understanding properties of the rooms relies on the analysis of reflections that compose the AIRs and the decay and absorption of the sound energy in the room. This thesis proposes novel methods for representing the early reflections, which are strong and sparse in nature and depend on the position of the source and the receiver. The resulting representation significantly reduces the coefficients needed to represent the AIR and can be combined with a stochastic model from the literature to also represent the late reflections. The use of Finite Impulse Response (FIR) for the task of classifying rooms is investigated, which provides novel results in this field. The aforementioned issues related to AIRs are highlighted through the analysis. This leads to the proposal of a data augmentation method for the training of the classifiers based on Generative Adversarial Networks (GANs), which uses existing data to create artificial AIRs, as if they were measured in real rooms. The networks learn properties of the room in the space defined by the parameters of the low-dimensional representation that is proposed in this thesis.Open Acces

A review of state-of-the-art speech modelling methods for the parameterisation of expressive synthetic speech

This document will review a sample of available voice modelling and transformation techniques, in view of an application in expressive unit-selection based speech synthesis in the framework of the PAVOQUE project. The underlying idea is to introduce some parametric modification capabilities at the level of the synthesis system, in order to compensate for the sparsity and rigidity, in terms of available emotional speaking styles, of the databases used to define speech synthesis voices. For this work, emotion-related parametric modifications will be restricted to the domains of voice quality and prosody, as suggested by several reviews addressing the vocal correlates of emotions (Schröder, 2001; Schröder, 2004; Roehling et al., 2006). The present report will start with a review of some techniques related to voice quality modelling and modification. First, it will explore the techniques related to glottal flow modelling. Then, it will review the domain of cross-speaker voice transformations, in view of a transposition to the domain of cross-emotion voice transformations. This topic will be exposed from the perspective of the parametric spectral modelling of speech and then from the perspective of available spectral transformation techniques. Then, the domain of prosodic parameterisation and modification will be reviewed

Singing voice analysis/synthesis

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2003.Includes bibliographical references (p. 109-115).The singing voice is the oldest and most variable of musical instruments. By combining music, lyrics, and expression, the voice is able to affect us in ways that no other instrument can. As listeners, we are innately drawn to the sound of the human voice, and when present it is almost always the focal point of a musical piece. But the acoustic flexibility of the voice in intimating words, shaping phrases, and conveying emotion also makes it the most difficult instrument to model computationally. Moreover, while all voices are capable of producing the common sounds necessary for language understanding and communication, each voice possesses distinctive features independent of phonemes and words. These unique acoustic qualities are the result of a combination of innate physical factors and expressive characteristics of performance, reflecting an individual's vocal identity. A great deal of prior research has focused on speech recognition and speaker identification, but relatively little work has been performed specifically on singing. There are significant differences between speech and singing in terms of both production and perception. Traditional computational models of speech have focused on the intelligibility of language, often sacrificing sound quality for model simplicity. Such models, however, are detrimental to the goal of singing, which relies on acoustic authenticity for the non-linguistic communication of expression and emotion. These differences between speech and singing dictate that a different and specialized representation is needed to capture the sound quality and musicality most valued in singing.(cont.) This dissertation proposes an analysis/synthesis framework specifically for the singing voice that models the time-varying physical and expressive characteristics unique to an individual voice. The system operates by jointly estimating source-filter voice model parameters, representing vocal physiology, and modeling the dynamic behavior of these features over time to represent aspects of expression. This framework is demonstrated to be useful for several applications, such as singing voice coding, automatic singer identification, and voice transformation.by Youngmoo Edmund Kim.Ph.D

Novel multiscale methods for nonlinear speech analysis

Cette thèse présente une recherche exploratoire sur l'application du Formalisme Microcanonique Multiéchelles (FMM) à l'analyse de la parole. Dérivé de principes issus en physique statistique, le FMM permet une analyse géométrique précise de la dynamique non linéaire des signaux complexes. Il est fondé sur l'estimation des paramètres géométriques locaux (les exposants de singularité) qui quantifient le degré de prédictibilité à chaque point du signal. Si correctement définis est estimés, ils fournissent des informations précieuses sur la dynamique locale de signaux complexes. Nous démontrons le potentiel du FMM dans l'analyse de la parole en développant: un algorithme performant pour la segmentation phonétique, un nouveau codeur, un algorithme robuste pour la détection précise des instants de fermeture glottale, un algorithme rapide pour l analyse par prédiction linéaire parcimonieuse et une solution efficace pour l approximation multipulse du signal source d'excitation.This thesis presents an exploratory research on the application of a nonlinear multiscale formalism, called the Microcanonical Multiscale Formalism (the MMF), to the analysis of speech signals. Derived from principles in Statistical Physics, the MMF allows accurate analysis of the nonlinear dynamics of complex signals. It relies on the estimation of local geometrical parameters, the singularity exponents (SE), which quantify the degree of predictability at each point of the signal domain. When correctly defined and estimated, these exponents can provide valuable information about the local dynamics of complex signals and has been successfully used in many applications ranging from signal representation to inference and prediction.We show the relevance of the MMF to speech analysis and develop several applications to show the strength and potential of the formalism. Using the MMF, in this thesis we introduce: a novel and accurate text-independent phonetic segmentation algorithm, a novel waveform coder, a robust accurate algorithm for detection of the Glottal Closure Instants, a closed-form solution for the problem of sparse linear prediction analysis and finally, an efficient algorithm for estimation of the excitation source signal.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Classification and Separation Techniques based on Fundamental Frequency for Speech Enhancement

[ES] En esta tesis se desarrollan nuevos algoritmos de clasificación y mejora de voz basados en las propiedades de la frecuencia fundamental (F0) de la señal vocal. Estas propiedades permiten su discriminación respecto al resto de señales de la escena acústica, ya sea mediante la definición de características (para clasificación) o la definición de modelos de señal (para separación). Tres contribuciones se aportan en esta tesis: 1) un algoritmo de clasificación de entorno acústico basado en F0 para audífonos digitales, capaz de clasificar la señal en las clases voz y no-voz; 2) un algoritmo de detección de voz sonora basado en la aperiodicidad, capaz de funcionar en ruido no estacionario y con aplicación a mejora de voz; 3) un algoritmo de separación de voz y ruido basado en descomposición NMF, donde el ruido se modela de una forma genérica mediante restricciones matemáticas.[EN]This thesis is focused on the development of new classification and speech enhancement algorithms based, explicitly or implicitly, on the fundamental frequency (F0). The F0 of speech has a number of properties that enable speech discrimination from the remaining signals in the acoustic scene, either by defining F0-based signal features (for classification) or F0-based signal models (for separation). Three main contributions are included in this work: 1) an acoustic environment classification algorithm for hearing aids based on F0 to classify the input signal into speech and nonspeech classes; 2) a frame-by-frame basis voiced speech detection algorithm based on the aperiodicity measure, able to work under non-stationary noise and applicable to speech enhancement; 3) a speech denoising algorithm based on a regularized NMF decomposition, in which the background noise is described in a generic way with mathematical constraints.Tesis Univ. Jaén. Departamento de Ingeniería de Telecomunición. Leída el 11 de enero de 201

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

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