Francis Grenez

Estimation of vocal dysperiodicities in disordered connected speech by means of distant-sample bidirectional linear predictive analysis

The article presents an analysis of vocal dysperiodicities in connected speech produced by dysphonic speakers. The processing is based on a comparison of the present speech fragment with future and past fragments. The size of the dysperiodicity estimate is zero for periodic speech signals. A feeble increase of the vocal dysperiodicity is guaranteed to produce a feeble increase of the estimate. No spurious noise boosting occurs owing to cycle insertion and omission errors, or phonetic segment boundary artifacts. Additional objectives of the study have been investigating whether deviations from periodicity are larger or more commonplace in connected speech than in sustained vowels, and whether sentences that comprise frequent voice onsets and offsets are noisier than sentences that comprise few. The corpora contain sustained vowels as well as grammatically- and phonetically matched sentences. An acoustic marker that correlates with the perceived degree of hoarseness summarizes the size of the dysperiodicities. The marker values for sustained vowels have been highly correlated with those for connected speech, and the marker values for sentences that comprise few voiced/unvoiced transients have been highly correlated with the marker values for sentences that comprise many.

Development and perceptual assessment of a synthesizer of disordered voices.

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.

Time-frequency analysis and instantaneous frequency estimation using two-sided linear prediction

This paper presents a new time-frequency distribution which uses a time-dependent two-sided linear predictor model. The current sample is estimated as a weighted sum of the past and future values. The two-sided linear prediction approach yields a smaller prediction error than that obtained by using the usual one-sided linear predictor model. To estimate the time-dependent coefficients of the two-sided linear predictor, these are expanded as a linear combination of a set of time functions basis which leads to an ensemble of equations of the type of Yule-Walker equations. The nonstationary power spectrum estimate is used as a time-frequency distribution to characterize the signal jointly in the time domain and the frequency domain. We show that two-sided prediction-based time-frequency distribution can discriminate two close components in the time-frequency plane that neither Choi-Williams distribution nor one-sided prediction-based time-frequency distribution are capable of resolving. Also, the proposed time-frequency distribution is used to estimate the instantaneous frequency. Examples show that the proposed approach outperforms the usual technique based on the nonstationary autoregressive model.

Multi-band dysperiodicity analyses of disordered connected speech

The objective is to analyse vocal dysperiodicities in connected speech produced by dysphonic speakers. The analysis involves a variogram-based method that enables tracking instantaneous vocal dysperiodicities. The dysperiodicity trace is summarized by means of the signal-to-dysperiodicity ratio, which has been shown to correlate strongly with the perceived degree of hoarseness of the speaker. Previously, this method has been evaluated on small corpora only. In this article, analyses have been carried out on two corpora comprising over 250 and 700 speakers. This has enabled carrying out multi-frequency band and multi-cue analyses without risking overfitting. The analysis results are compared to the cepstral peak prominence, which is a popular cue that indirectly summarizes vocal dysperiodicities frame-wise. A perceptual rating has been available for the first corpus whereas speakers in the second corpus have been categorized as normal or pathological only. For the first corpus, results show that the correlation with perceptual scores increases statistically significantly for multi-band analysis compared to conventional full-band analysis. Also, combining the cepstral peak prominence with the low-frequency band signal-to-dysperiodicity ratio statistically significantly increases their combined correlation with perceptual scores. The signal-to-dysperiodicity ratios of the two corpora have been separately submitted to principal component analysis. The results show that the first two principal components are interpretable in terms of the degree of dysphonia and the spectral slope, respectively. The clinical relevance of the principal components has been confirmed by linear discriminant analysis.

Estimation of dysperiodicities in disordered speech

This paper presents two methods for tracking vocal dysperiodicities in connected speech. The first is based on a long-term linear predictor with one coefficient and the second on a generalized variogram. Both analysis methods guarantee that a slight increase or decrease of irregularities in the speech signal produces a slight increase or decrease of the estimated vocal dysperiodicity trace. No spurious noise boosting occurs owing to erroneous insertions or omissions of speech cycles, or the comparison of speech cycles across phonetic boundaries. The two techniques differ with regard to how slow changes of speech cycle amplitudes are compensated for. They are compared on two speech corpora. One comprises stationary fragments of vowel [a] produced by 89 male and female normophonic and dysphonic speakers. Another comprises four French sentences as well as vowel [a] produced by 22 male and female normophonic and dysphonic speakers. Vocal dysperiodicities are summarized by means of global and segmental signal-to-dysperiodicity ratios. They are correlated with hoarseness scores obtained by means of perceptual ratings of the speech tokens. The two techniques obtain signal-to-dysperiodicity ratios that are statistically significantly correlated with the hoarseness scores. For connected speech, the segmental signal-to-dysperiodicity ratio correlates more strongly with perceptual scores of hoarseness than the global signal-to-dysperiodicity ratio.

Dysphonic speech analysis using generalized variogram

Acoustic analyses of speech signals are popular in the framework of the clinical evaluation of voice and the diagnosis of disease. We propose a new strategy for dysphonic speech analysis that extracts vocal dysperiocities by using a generalized form of the variogram. The generalized variogram allows the inherent drawbacks of both long-term and short-term linear prediction formulations, widely used in disordered speech analysis, to be overcome. The proposed approach uses a forgetting factor to account for the nonstationarity nature of the speech signal. Experimental results show that the proposed approach outperforms the double prediction-based technique.

Fundamental frequency estimation and vocal tremor analysis by means of Morlet wavelet transforms

A vocal frequency estimation method and an analysis of vocal tremor are proposed. Vocal tremor is a narrow-band low-frequency perturbation of the vocal frequency. The vocal frequency estimate is the instantaneous frequency calculated in an automatically selected frequency-band of a wavelet transform of the speech signal. The vocal frequency estimation method is compared to an event-based method and a Hilbert-transform method for speech signals. The tremor frequency and amplitude are obtained by means of a continuous wavelet transform applied to the instantaneous frequency trace. The proposed analysis has been applied to parkinsonian and normophonic speakers. The results suggest that the vocal tremor features differ for both groups.

NLOS-multipath effects on Pseudo-Range estimation in urban canyons for GNSS applications

Pseudo-range (PR) errors due to NLOS-Multipath (non-line-of-sight-multipath) are studied in an urban canyon model. In order to determine the different reflected and diffracted rays which compose the NLOS-multipath, a dedicated ray tracing algorithm is applied. Two different methods are used in order to compute the PR error. The first one uses the error due to the maximum power ray and the second one uses an early minus late (E-L) receiver model. Simulations in different urban canyon configurations are carried out in order to obtain PR error distributions and associated probabilities due to NLOS-multipath rays above a given power threshold.

Chebyshev design of filters for subband coders

Linear phase and minimum phase quadrature mirror filters, with prescribed stopband attenuation and optimum reconstruction error, are designed by linear programming. The variation of the reconstruction error as a function of the filter is investigated on an example. >

Early detection of epileptic seizures based on parameter identification of neural mass model

Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters. The algorithm was evaluated against the manual scoring of a human expert on intracranial EEG samples from 16 patients suffering from different types of epilepsy. Results suggest that the algorithm is best suited for patients suffering from temporal lobe epilepsy (sensitivity was 95.0% ± 10.0% and false positive rate was 0.20 ± 0.22 per hour).