Laurent Couvreur

Chirp group delay analysis of speech signals

This study proposes new group delay estimation techniques that can be used for analyzing resonance patterns of short-term discrete-time signals and more specifically speech signals. Phase processing or equivalently group delay processing of speech signals are known to be difficult due to large spikes in the phase/group delay functions that mask the formant structure. In this study, we first analyze in detail the z-transform zero patterns of short-term speech signals in the z-plane and discuss the sources of spikes on group delay functions, namely the zeros closely located to the unit circle. We show that windowing largely influences these patterns, therefore short-term phase processing. Through a systematic study, we then show that reliable phase/group delay estimation for speech signals can be achieved by appropriate windowing and group delay functions can reveal formant information as well as some of the characteristics of the glottal flow component in speech signals. However, such phase estimation is highly sensitive to noise and robust extraction of group delay based parameters remains difficult in real acoustic conditions even with appropriate windowing. As an alternative, we propose processing of chirp group delay functions, i.e. group delay functions computed on a circle other than the unit circle in z-plane, which can be guaranteed to be spike-free. We finally present one application in feature extraction for automatic speech recognition (ASR). We show that chirp group delay representations are potentially useful for improving ASR performance.

Facial expression classification: An approach based on the fusion of facial deformations using the transferable belief model

A method for the classification of facial expressions from the analysis of facial deformations is presented. This classification process is based on the transferable belief model (TBM) framework. Facial expressions are related to the six universal emotions, namely Joy, Surprise, Disgust, Sadness, Anger, Fear, as well as Neutral. The proposed classifier relies on data coming from a contour segmentation technique, which extracts an expression skeleton of facial features (mouth, eyes and eyebrows) and derives simple distance coefficients from every face image of a video sequence. The characteristic distances are fed to a rule-based decision system that relies on the TBM and data fusion in order to assign a facial expression to every face image. In the proposed work, we first demonstrate the feasibility of facial expression classification with simple data (only five facial distances are considered). We also demonstrate the efficiency of TBM for the purpose of emotion classification. The TBM based classifier was compared with a Bayesian classifier working on the same data. Both classifiers were tested on three different databases.

Blind Model Selection for Automatic Speech Recognition in Reverberant Environments

This communication presents a new method for automatic speech recognition in reverberant environments. Our approach consists in the selection of the best acoustic model out of a library of models trained on artificially reverberated speech databases corresponding to various reverberant conditions. Given a speech utterance recorded within a reverberant room, a Maximum Likelihood estimate of the fullband room reverberation time is computed using a statistical model for short-term log-energy sequences of anechoic speech. The estimated reverberation time is then used to select the best acoustic model, i.e., the model trained on a speech database most closely matching the estimated reverberation time, which serves to recognize the reverberated speech utterance. The proposed model selection approach is shown to improve significantly recognition accuracy for a connected digit task in both simulated and real reverberant environments, outperforming standard channel normalization techniques.

Facial expression recognition based on the belief theory: comparison with different classifiers

This paper presents a system for classifying facial expressions based on a data fusion process relying on the Belief Theory (BeT). Four expressions are considered: joy, surprise, disgust as well as neutral. The proposed system is able to take into account intrinsic doubt about emotion in the recognition process and to handle the fact that each person has his/her own maximal intensity of displaying a particular facial expression. To demonstrate the suitability of our approach for facial expression classification, we compare it with two other standard approaches: the Bayesian Theory (BaT) and the Hidden Markov Models (HMM). The three classification systems use characteristic distances measuring the deformations of facial skeletons. These skeletons result from a contour segmentation of facial permanent features (mouth, eyes and eyebrows). The performances of the classification systems are tested on the Hammal-Caplier database [1] and it is shown that the BeT classifier outperforms both the BaT and HMM classifiers for the considered application.

An Agent-Based Multimodal Interface for Sketch Interpretation

We present a multimodal interface for sketch interpretation that relies on a multi-agent architecture. The design of the interpretation engine and the different agents are based on a user-centered approach where efficiency measure is defined as user satisfaction. So far, several graphical agents have been implemented for recognizing basic graphical objects (e.g. lines, circles, etc.) as well as more complex (e.g., hatches, stairs, captions, etc) in architectural design. Besides, vocal agents have been developed for recognizing spoken annotations (e.g. dimensions) and interface commands. Realistic evaluations with professional users have demonstrated the potential interest of the proposed system

Spectral analysis of speech signals using chirp group delay

This study presents chirp group delay processing techniques for spectral analysis of speech signals. It is known that group delay processing is potentially very useful for spectral analysis of speech signals. However, it is also well known that group delay processing is difficult due to large spikes that mask the formant structure. In this chapter, we first discuss the sources of spikes on group delay functions, namely the zeros closely located to the unit circle. We then propose processing of chirp group delay functions, i.e. group delay functions computed on a circle other than the unit circle in z-plane. Chirp group delay functions can be guaranteed to be spike-free if zero locations can be controlled. The technique we use here for that is to compute the zero-phased version of the signal for which the zeros appear very close (or on) the unit circle. The final representation obtained is named as the chirp group delay of zero-phased version of a signal (CGDZP). We demonstrate use of CGDZP in two applications: formant tracking and feature extraction for automatic speech recognition (ASR). We show that high quality formant tracking can be performed by simply picking peaks on CGDZP and CGDZP is potentially useful for improving ASR performance.