13 research outputs found

    Improving text-independent phonetic segmentation based on the Microcanonical Multiscale Formalism

    Get PDF
    International audienceIn an earlier work, we proposed a novel phonetic segmentation method based on speech analysis under the Microcanonical Multiscale Formalism (MMF). The latter relies on the computation of local geometrical parameters, singularity exponents (SE). We showed that SE convey valuable information about the local dynamics of speech that can readily and simply used to detect phoneme boundaries. By performing error analysis of our original algorithm, in this paper we propose a 2-steps technique which better exploits SE to improve the segmentation accuracy. In the first step, we detect the boundaries of the original signal and of a low-pass filtred version, and we consider the union of all detected boundaries as candidates. In the second step, we use a hypothesis test over the local SE distribution of the original signal to select the final boundaries. We carry out a detailed evaluation and comparison over the full training set of the TIMIT database which could be useful to other researchers for comparison purposes. The results show that the new algorithm not only outperforms the original one, but also is significantly much more accurate than state-of-the-art ones

    Phonetic segmentation of speech signal using local singularity analysis

    Get PDF
    International audienceThis paper presents the application of a radically novel approach, called the Microcanonical Multiscale Formalism (MMF) to speech analysis. MMF is based on precise estimation of local scaling parameters that describe the inter-scale correlations at each point in the signal domain and provides e cient means for studying local non-linear dynamics of complex signals. In this paper we introduce an e cient way for estimation of these parameters and then, we show that they convey relevant information about local dynamics of the speech signal that can be used for the task of phonetic segmentation. We thus develop a two-stage segmentation algorithm: for the first step, we introduce a new dynamic programming technique to e ciently generate an initial list of phoneme-boundary candidates and in the second step, we use hypothesis testing to refine the initial list of candidates. We present extensive experiments on the full TIMIT database. The results show that our algorithm is significantly more accurate than state-of-the-art ones

    Pitch-based speech perturbation measures using a novel GCI detection algorithm: Application to pathological voice classification

    Get PDF
    International audienceClassical pitch-based perturbation measures, such as Jitter and Shimmer, are generally based on detection algorithms of pitch marks which assume the existence of a periodic pitch pattern and/or rely on the linear source-filter speech model. While these assumptions can hold for normal speech, they are generally not valid for pathological speech. The latter can indeed present strong aperiodicity, nonlinearity and turbulence/noise. Recently, we introduced on a novel nonlinear algorithm for Glottal Closure Instants (GCI) detection which has the strong advantage of not making such assumptions. In this paper, we use this new algorithm to compute standard pitch-based perturbation measures and compare its performances to the widely used tool PRAAT. We address the task of classification between normal and pathological speech, and carry out the experiments using the popular MEEI database. The results show that our algorithm leads to significantly higher classification accuracy than PRAAT. Moreover, some important statistical features become significantly discriminative, while they are meaningless when using PRAAT (in the sense that they have almost no discrimination power)

    Novel multiscale methods for nonlinear speech analysis

    Get PDF
    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

    Efficient multipulse approximation of speech excitation using the most singular manifold

    Get PDF
    INTERSPEECH 2012We propose a novel approach to find the locations of the multipulse sequence that approximates the speech source excitation. This approach is based on the notion of Most Singular Manifold (MSM) which is associated to the set of less predictable events. The MSM is formed by identifying (directly from the speech waveform) multiscale singularities which may correspond to significant impulsive excitations of the vocal tract. This identification is done through a multiscale measure of local predictability and the estimation of its associated singularity exponents. Once the pulse locations are found using the MSM, their amplitudes are computed using the second stage of the classical MultiPulse Excitation (MPE) coder. The multipulse sequence is then fed to the classical LPC synthesizer to reconstruct speech. The resulting MSM-based algorithm is shown to be significantly more efficient than MPE. We evaluate our algorithm using 1 hour of speech from the TIMIT database and compare its performances to MPE and a recent approach based on compressed sensing (CS). The results show that our algorithm yields similar perceptual quality as MPE and outperforms the CS method when the number of pulses is low

    Detection of Glottal Closure Instants based on the Microcanonical Multiscale Formalism

    Get PDF
    International audienceThis paper presents a novel algorithm for automatic detection of Glottal Closure Instants (GCI) from the speech signal. Our approach is based on a novel multiscale method that relies on precise estimation of a multiscale parameter at each time instant in the signal domain. This parameter quantifies the degree of signal singularity at each sample from a multi-scale point of view and thus its value can be used to classify signal samples accordingly. We use this property to develop a simple algorithm for detection of GCIs and we show that for the case of clean speech, our algorithm performs almost as well as a recent state-of-the-art method. Next, by performing a comprehensive comparison in presence of 14 different types of noises, we show that our method is more accurate (particularly for very low SNRs). Our method has lower computational times compared to others and does not rely on an estimate of pitch period or any critical choice of parameters

    Phoneme segmentation and Voice activity detection

    Get PDF
    This internship was intended to be a continuation of my work last year with the same team, whose focus is non-linear methods for complex signal analysis using concepts of scale invariance and particularly the development of a new multiscale microcanonical formalism (MMF). While the fields of application of this new formalism are diverse, one of them is speech processing. My contribution was exploratory research into innovative methods for text-independent phoneme segmentation which conform to a "linear" model, the goal being to provide a performance comparison with the "non-linear" MMF-based methods under development by the other team members. This year I focused on two areas: a continuation of last year's work in phoneme segmentation, and implementation of voice activity detection algorithms. For the continuation of last year's work, I performed experiments with more rigor in order to better understand the results I obtained last year. I re-examined the algorithms I implemented last year and corrected discrepancies, and brought the implementations closer into line with standard practice. Some of the work to this end is described in a section in the Appendix A. I performed the requisite experiments to evaluate the performance of these methods on a standard database used for phoneme segmentation. I continued past this point with experiments on two other segmentation methods, in preparation for publication of a comprehensive journal paper. I made improvements to the functioning some of these methods, and in some instances I was able to improve the performance of the algorithms. In addition to phoneme segmentation, the team is interested in applying the MMF to the field of Voice Activity Detection (VAD). It was desired that I implement several so-called "classical" VAD algorithms to serve as a basis for comparison for the new, non-linear algorithms which will be developed by the team in the future. As such I implemented four VAD algorithms commonly used as references in the literature to function as a standard reference for the new methods being developed. Further, I implemented a framework for evaluation of VAD algorithms. This consisted in devising methods for generating test databases for use in evaluating the performance of VAD algorithms and implementing them in code. Also under this effort, I wrote programs for scoring the output of these algorithms. I adapted existing code for two standard VADs to function within this framework, and finally evaluated these VADs under different conditions

    A Novel text-independent phonetic segmentation algorithm based on the Microcanonical Multiscale Formalism

    No full text
    International audienceWe propose a radically novel approach to analyze speech signals from a statistical physics perspective. Our approach is based on a new framework, the Microcanonical Multiscale Formalism (MMF), which is based on the computation of singularity exponents, defined at each point in the signal domain. The latter allows nonlinear analysis of complex dynamics and, particularly, characterizes the intermittent signature. We study the validity of the MMF for the speech signal and show that singularity exponents convey indeed valuable information about its local dynamics. We define an accumulative measure on the exponents which reveals phoneme boundaries as the breaking points of a piecewise linear-like curve. We then develop a simple automatic phonetic segmentation algorithm using piecewise linear curve fitting. We present experiments on the full TIMIT database. The results show that our algorithm yields considerably better accuracy than recently published ones
    corecore