GLOTTAL EXCITATION EXTRACTION OF VOICED SPEECH - JOINTLY PARAMETRIC AND NONPARAMETRIC APPROACHES

The goal of this dissertation is to develop methods to recover glottal flow pulses, which contain biometrical information about the speaker. The excitation information estimated from an observed speech utterance is modeled as the source of an inverse problem. Windowed linear prediction analysis and inverse filtering are first used to deconvolve the speech signal to obtain a rough estimate of glottal flow pulses. Linear prediction and its inverse filtering can largely eliminate the vocal-tract response which is usually modeled as infinite impulse response filter. Some remaining vocal-tract components that reside in the estimate after inverse filtering are next removed by maximum-phase and minimum-phase decomposition which is implemented by applying the complex cepstrum to the initial estimate of the glottal pulses. The additive and residual errors from inverse filtering can be suppressed by higher-order statistics which is the method used to calculate cepstrum representations. Some features directly provided by the glottal source\u27s cepstrum representation as well as fitting parameters for estimated pulses are used to form feature patterns that were applied to a minimum-distance classifier to realize a speaker identification system with very limited subjects

Testing the assumptions of linear prediction analysis in normal vowels

This paper develops an improved surrogate data test to show experimental evidence, for all the simple vowels of US English, for both male and female speakers, that Gaussian linear prediction analysis, a ubiquitous technique in current speech technologies, cannot be used to extract all the dynamical structure of real speech time series. The test provides robust evidence undermining the validity of these linear techniques, supporting the assumptions of either dynamical nonlinearity and/or non-Gaussianity common to more recent, complex, efforts at dynamical modelling speech time series. However, an additional finding is that the classical assumptions cannot be ruled out entirely, and plausible evidence is given to explain the success of the linear Gaussian theory as a weak approximation to the true, nonlinear/non-Gaussian dynamics. This supports the use of appropriate hybrid linear/nonlinear/non-Gaussian modelling. With a calibrated calculation of statistic and particular choice of experimental protocol, some of the known systematic problems of the method of surrogate data testing are circumvented to obtain results to support the conclusions to a high level of significance

Third-order cumulant-based wiener filtering algorithm applied to robust speech recognition

In previous works [5], [6], we studied some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim-Oppenheim [2], where the AR spectral estimation of the speech is carried out using a second-order analysis. But in our algorithms we consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors: a cumulant-based Wiener Filtering (AR3_IF) is applied to Robust Speech Recognition. A low complexity approach of this algorithm is tested in presence of bathroom water noise and its performance is compared to classical Spectral Subtraction method. Some results are presented when training task of the speech recognition system (HTK-MFCC) is executed under clean and noisy conditions. These results show a lower sensitivity to the presence of water noise when applying AR3_IF algorithm inside of a speech recognition task.Peer ReviewedPostprint (published version

Some robust speech enhancement techniques using higher order AR estimation

Peer ReviewedPostprint (published version

Improving Quality of Life: Home Care for Chronically Ill and Elderly People

In this chapter, we propose a system especially created for elderly or chronically ill people that are with special needs and poor familiarity with technology. The system combines home monitoring of physiological and emotional states through a set of wearable sensors, user-controlled (automated) home devices, and a central control for integration of the data, in order to provide a safe and friendly environment according to the limited capabilities of the users. The main objective is to create the easy, low-cost automation of a room or house to provide a friendly environment that enhances the psychological condition of immobilized users. In addition, the complete interaction of the components provides an overview of the physical and emotional state of the user, building a behavior pattern that can be supervised by the care giving staff. This approach allows the integration of physiological signals with the patient’s environmental and social context to obtain a complete framework of the emotional states

Survey of Noise Estimation Algorithms for Speech Enhancement Using Spectral Subtraction

Speech enhancement means speech improvement. Actually the speech enhancement is performed by using various techniques and different algorithms. Over the past several years there has been attention focused on the problem of enhancement of speech degraded by additive background noise. For many applications background suppression is required. The spectral - subtractive algorithm is one of the first algorithm proposed for additive background noise and it has gone through many modifications with time. For spectral subtraction method noise estimation is important for that there are various noise estimation algorithms. All these noise estimation algorithms are important for removing background noise

Comparison of different order cumulants in a speech enhancement system by adaptive Wiener filtering

The authors study some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim and Oppenheim (1978), where the AR spectral estimation of the speech is carried out using a second-order analysis. But in their algorithms the authors consider an AR estimation by means of a cumulant (third- and fourth-order) analysis. The authors provide a behavior comparison between the cumulant algorithms and the classical autocorrelation one. Some results are presented considering the noise (additive white Gaussian noises) that allows the best improvement and those noises (diesel engine and reactor noise) that leads to the worst one. And exhaustive empirical test shows that cumulant algorithms outperform the original autocorrelation algorithm, specially at low SNR.Peer ReviewedPostprint (published version

Identification of persons via voice imprint

Tato práce se zabývá textově závislým rozpoznáváním řečníků v systémech, kde existuje pouze omezené množství trénovacích vzorků. Pro účel rozpoznávání je navržen otisk hlasu založený na různých příznacích (např. MFCC, PLP, ACW atd.). Na začátku práce je zmíněn způsob vytváření řečového signálu. Některé charakteristiky řeči, důležité pro rozpoznávání řečníků, jsou rovněž zmíněny. Další část práce se zabývá analýzou řečového signálu. Je zde zmíněno předzpracování a také metody extrakce příznaků. Následující část popisuje proces rozpoznávání řečníků a zmiňuje způsoby ohodnocení používaných metod: identifikace a verifikace řečníků. Poslední teoreticky založená část práce se zabývá klasifikátory vhodnými pro textově závislé rozpoznávání. Jsou zmíněny klasifikátory založené na zlomkových vzdálenostech, dynamickém borcení časové osy, vyrovnávání rozptylu a vektorové kvantizaci. Tato práce pokračuje návrhem a realizací systému, který hodnotí všechny zmíněné klasifikátory pro otisk hlasu založený na různých příznacích.This work deals with the text-dependent speaker recognition in systems, where just a few training samples exist. For the purpose of this recognition, the voice imprint based on different features (e.g. MFCC, PLP, ACW etc.) is proposed. At the beginning, there is described the way, how the speech signal is produced. Some speech characteristics important for speaker recognition are also mentioned. The next part of work deals with the speech signal analysis. There is mentioned the preprocessing and also the feature extraction methods. The following part describes the process of speaker recognition and mentions the evaluation of the used methods: speaker identification and verification. Last theoretically based part of work deals with the classifiers which are suitable for the text-dependent recognition. The classifiers based on fractional distances, dynamic time warping, dispersion matching and vector quantization are mentioned. This work continues by design and realization of system, which evaluates all described classifiers for voice imprint based on different features.