Novel Pitch Detection Algorithm With Application to Speech Coding

This thesis introduces a novel method for accurate pitch detection and speech segmentation, named Multi-feature, Autocorrelation (ACR) and Wavelet Technique (MAWT). MAWT uses feature extraction, and ACR applied on Linear Predictive Coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions

Speech coding at medium bit rates using analysis by synthesis techniques

Speech coding at medium bit rates using analysis by synthesis technique

Sparsity in Linear Predictive Coding of Speech

nrpages: 197status: publishe

Perceptual models in speech quality assessment and coding

The ever-increasing demand for good communications/toll quality speech has created a renewed interest into the perceptual impact of rate compression. Two general areas are investigated in this work, namely speech quality assessment and speech coding. In the field of speech quality assessment, a model is developed which simulates the processing stages of the peripheral auditory system. At the output of the model a "running" auditory spectrum is obtained. This represents the auditory (spectral) equivalent of any acoustic sound such as speech. Auditory spectra from coded speech segments serve as inputs to a second model. This model simulates the information centre in the brain which performs the speech quality assessment. [Continues.

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

A robust low bit rate quad-band excitation LSP vocoder.

by Chiu Kim Ming.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 103-108).Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Speech production --- p.2Chapter 1.2 --- Low bit rate speech coding --- p.4Chapter Chapter 2 --- Speech analysis & synthesis --- p.8Chapter 2.1 --- Linear prediction of speech signal --- p.8Chapter 2.2 --- LPC vocoder --- p.11Chapter 2.2.1 --- Pitch and voiced/unvoiced decision --- p.11Chapter 2.2.2 --- Spectral envelope representation --- p.15Chapter 2.3 --- Excitation --- p.16Chapter 2.3.1 --- Regular pulse excitation and Multipulse excitation --- p.16Chapter 2.3.2 --- Coded excitation and vector sum excitation --- p.19Chapter 2.4 --- Multiband excitation --- p.22Chapter 2.5 --- Multiband excitation vocoder --- p.25Chapter Chapter 3 --- Dual-band and Quad-band excitation --- p.31Chapter 3.1 --- Dual-band excitation --- p.31Chapter 3.2 --- Quad-band excitation --- p.37Chapter 3.3 --- Parameters determination --- p.41Chapter 3.3.1 --- Pitch detection --- p.41Chapter 3.3.2 --- Voiced/unvoiced pattern generation --- p.43Chapter 3.4 --- Excitation generation --- p.47Chapter Chapter 4 --- A low bit rate Quad-Band Excitation LSP Vocoder --- p.51Chapter 4.1 --- Architecture of QBELSP vocoder --- p.51Chapter 4.2 --- Coding of excitation parameters --- p.58Chapter 4.2.1 --- Coding of pitch value --- p.58Chapter 4.2.2 --- Coding of voiced/unvoiced pattern --- p.60Chapter 4.3 --- Spectral envelope estimation and coding --- p.62Chapter 4.3.1 --- Spectral envelope & the gain value --- p.62Chapter 4.3.2 --- Line Spectral Pairs (LSP) --- p.63Chapter 4.3.3 --- Coding of LSP frequencies --- p.68Chapter 4.3.4 --- Coding of gain value --- p.77Chapter Chapter 5 --- Performance evaluation --- p.80Chapter 5.1 --- Spectral analysis --- p.80Chapter 5.2 --- Subjective listening test --- p.93Chapter 5.2.1 --- Mean Opinion Score (MOS) --- p.93Chapter 5.2.2 --- Diagnostic Rhyme Test (DRT) --- p.96Chapter Chapter 6 --- Conclusions and discussions --- p.99References --- p.103Appendix A Subroutine of pitch detection --- p.A-I - A-IIIAppendix B Subroutine of voiced/unvoiced decision --- p.B-I - B-VAppendix C Subroutine of LPC coefficients calculation using Durbin's recursive method --- p.C-I - C-IIAppendix D Subroutine of LSP calculation using Chebyshev Polynomials --- p.D-I - D-IIIAppendix E Single syllable word pairs for Diagnostic Rhyme Test --- p.E-

Speech coding

Speech is the predominant means of communication between human beings and since the invention of the telephone by Alexander Graham Bell in 1876, speech services have remained to be the core service in almost all telecommunication systems. Original analog methods of telephony had the disadvantage of speech signal getting corrupted by noise, cross-talk and distortion Long haul transmissions which use repeaters to compensate for the loss in signal strength on transmission links also increase the associated noise and distortion. On the other hand digital transmission is relatively immune to noise, cross-talk and distortion primarily because of the capability to faithfully regenerate digital signal at each repeater purely based on a binary decision. Hence end-to-end performance of the digital link essentially becomes independent of the length and operating frequency bands of the link Hence from a transmission point of view digital transmission has been the preferred approach due to its higher immunity to noise. The need to carry digital speech became extremely important from a service provision point of view as well. Modem requirements have introduced the need for robust, flexible and secure services that can carry a multitude of signal types (such as voice, data and video) without a fundamental change in infrastructure. Such a requirement could not have been easily met without the advent of digital transmission systems, thereby requiring speech to be coded digitally. The term Speech Coding is often referred to techniques that represent or code speech signals either directly as a waveform or as a set of parameters by analyzing the speech signal. In either case, the codes are transmitted to the distant end where speech is reconstructed or synthesized using the received set of codes. A more generic term that is applicable to these techniques that is often interchangeably used with speech coding is the term voice coding. This term is more generic in the sense that the coding techniques are equally applicable to any voice signal whether or not it carries any intelligible information, as the term speech implies. Other terms that are commonly used are speech compression and voice compression since the fundamental idea behind speech coding is to reduce (compress) the transmission rate (or equivalently the bandwidth) And/or reduce storage requirements In this document the terms speech and voice shall be used interchangeably