Francis Grenez's research while affiliated with Université Libre de Bruxelles and other places

The article concerns the interpretation of the principal components of the spectrogram of the speech signal and its application to the description of dysarthric speech. Each principal component is a linear combination of the frame spectra. We show that the first principal component of the spectrogram is closely related to the long-term average spectrum (LTAS) and the second principal component is the difference of two weighted sums of frame spectra reporting open and close vowel frame spectra respectively. We investigate articulation deficits in dysarthric speakers via cues obtained from principal components of the spectrogram of connected speech because long-term average spectra have been claimed to inform about speaker settings of the vocal tract.

The relevance of the harmonic-to-noise ratio (HNR) and glottal cycle length jitter as cues of the vocal timbre of Parkinson speakers is investigated. HNR and vocal cycle length jitter are known to be suitable cues for the evaluation of the vocal timbre of dysphonic speakers. However, the question whether they are relevant descriptors of the voice quality of Parkinson speakers is still unanswered. Empirical mode decomposition (EMD) has been used to estimate the HNR by decomposing the log-magnitude spectrum of the speech signal into its harmonic, contour and noise components. Cycle length jitter has been estimated via the break-up by empirical mode decomposition of the cycle length time series into the intonation contour as well as the perturbations owing to tremor and jitter. HNR and cycle length jitter values of vowels [a] sustained by 205 Parkinson and 74 control speakers are in the same interval respectively and the differences are not statistically significant. Also, the standard deviations of the per-frame HNR values of an utterance do not differ statistically significantly between control and Parkinson speakers.

This paper concerns the optimization of EEG signal parameters for epileptic seizure detection. In a previous study, a macroscopic model has been used to model various waveforms of EEG signal and to optimize its parameters by means of a genetic algorithm (GA). In the GA-based method for EEG parameters estimation, an optimization procedure is used. The aim of the optimization procedure is to minimize an objective function. The minimized error function compares the desired waveform (real EEG signal) and the waveform of the signal provided by the model both in the time domain and frequency domain. In the present study, we propose a time-scale based representation for the objective function as an alternative to the time and frequency based objective function used in the early study. The proposed objective function takes into account the non-stationary nature of the EEG signal. The performance of the proposed wavelet-based objective function is compared to that of the spectral objective function.

The presentation concerns a method that obtains the size and frequency of vocal tremor in speech sounds sustained by normal speakers and patients suffering from neurological disorders. The glottal cycle lengths are tracked in the temporal domain via salience analysis and dynamic programming. The cycle length time series is then decomposed into a sum of oscillating components by empirical mode decomposition the instantaneous envelopes and frequencies of which are obtained via an AM-FM decomposition. Based on their average instantaneous frequencies, the empirical modes are then assigned to four categories (intonation, physiological tremor, neurological tremor as well as jitter) and added within each. The within-category size of the cycle length perturbations is estimated via the standard deviation of the empirical mode sum divided by the average cycle length. The tremor frequency within the neurological tremor category is obtained via a weighted instantaneous average of the mode frequencies followed by a weighted temporal average. The method is applied to two corpora of vowels sustained by 123 and 74 control and 456 and 205 Parkinson speakers respectively

In this paper, empirical mode decomposition (EMD) is proposed as an alternative to decompose the log magnitude spectrum of the speech signal into its harmonic, envelope and noise components. The acoustic measure named harmonic-to-noise ratio (HNR) is used to summarize the degree of disturbance in the speech signal and consequently to evaluate the overall quality of the disordered voices produced by dysphonic speakers. Most approaches for HNR estimation have in common to involve the isolation of individual speech cycles or pseudo-harmonics/rhamonics in speech spectrum/cepstrum; however, this isolation cannot be carried out reliably in speech produced by severely hoarse speakers and may result in inaccurate HNR estimation. The EMD-based approach used in this study incorporates an appropriate procedure that estimates automatically the thresholds used by the clustering algorithm without knowledge of the fundamental frequency. The frequency range of the harmonic and noise components is divided into ten equally spaced intervals and the harmonic-to-noise ratios (HNRs) within each interval are used as independent variables to summarize the amount of perceived hoarseness. The proposed method is evaluated on a corpus comprising 251 normophonic and dysphonic speakers. Multiple correlation analysis carried out on HNRs from the different frequency bands shows that multi-band analysis based on empirical mode decomposition results in statistically significantly higher correlation of predicted scores with scores of perceived hoarseness over full-band analysis. Principal component analysis is carried out on the HNR measures obtained in the ten frequency bands. More than 97% of the total variance is explained by the first two principal components, PC1 and PC2. Experimental results show that the first principal component is interpretable in terms of the degree of the severity of hoarseness whereas the second principal component indicates whether the voice is high-pitched or low-pitched. It is shown that the first two principal components result in a high predictability of hoarseness scores.

A synthesizer is based on a nonlinear wave-shaping model of the glottal area, an algebraic model of the glottal aerodynamics as well as concatenated-tube models of the trachea and vocal tract. Voice disorders are simulated by way of models of vocal frequency jitter and tremor, vocal amplitude shimmer and tremor, as well as pulsatile additive noise. Six experiments have been carried out to assess the synthesizer perceptually. Three experiments involve the perceptual categorization of male synthetic and human stimuli and one the auditory discrimination between synthetic and human tokens. A fifth experiment reports the auditory discrimination between synthetic tokens with different levels of additive and modulation noise. A sixth experiment reports the scoring by expert listeners of male synthetic stimuli on equal-appearing interval scales grade-roughness-breathiness (GRB). A first objective is to demonstrate the ability of the synthesizer to simulate vowel sounds that are valid exemplars of speech sounds produced by humans with voice disorders. A second objective is to learn how human expert raters perceptually map vocal frequency, additive and modulation noise as well as vowel categories into scores on GRB scales.