Jean-Paul Haton

A recombination model for multi-band speech recognition

We describe a continuous speech recognition system that uses the multi-band paradigm. This principle is based on the recombination of several independent sub-recognizers, each one assigned to a specific frequency band. The major issue of such systems consists of deciding at which time the recombination must be done. Our algorithm lets each band be totally independent from the others, and uses the different solutions to resegment the initial sentence. Finally, the bands are synchronously merged together, according to this new segmentation. The whole system is too complex to be entirely described here, and, in this paper, we concentrate on the synchronous recombination part, which is achieved by a classifier. The system has been tested in clean and noisy environments, and proved to be especially robust to noise.

APHODEX, design and implementation of an acoustic-phonetic decoding expert system

The acoustic-phonetic decoding of complete sentences is a major bottleneck in continuous speech recognition. Our group, who has been working in this area for the past twelve years, first developed systems based on synchronous centi-second pattern-matching. In order to improve the accuracy of present phonetic decoders, we are now developing an expert production-rule system that should emulate the competence of an experienced spectrogram reader. After a brief description of the methodology used to elucidate the experts knowledge and strategies, the paper explains how this expertise has been implemented ; results from the present experimental version of our system (APHODEX) are discussed. In order to increase efficiency while maintaining the same degree of accuracy, we are concurrently designing a system in which the experts knowledge is represented by frames. An overall description of the system, which describes the phonetic decoding process in terms of a frame language is given in the second part of this paper.

A unified maximum likelihood approach to acoustic mismatch compensation: application to noisy Lombard speech recognition

In the context of continuous density hidden Markov model (CDHMM) we present a unified maximum likelihood (ML) approach to acoustic mismatch compensation. This is achieved by introducing additive Gaussian biases at the state level in both the mel cepstral and linear spectral domains. Flexible modelling of different mismatch effects can be obtained through appropriate bias tying. A maximum likelihood approach for joint estimation of both mel cepstral and linear spectral biases from the observed mismatched speech given only one set of clean speech models is presented, where the obtained bias estimates are used for the compensation of clean speech models during decoding. The proposed approach is applied to the recognition of noisy Lombard speech, and significant improvement in the word recognition rate is achieved.

APHODEX, AN ACOUSTIC-PHONETIC DECODING EXPERT SYSTEM

The acoustic-phonetic decoding of sentences is a major bottleneck in continuous speech recognition. Our group has been working in this area for the past twelve years. In order to improve the accura...

A new approach to design fuzzy controllers for mobile robots navigation

This article presents a new method to design, in two levels, fuzzy controller for reactive navigation of a mobile robot in a structured unknown environment. At the first level, adjacent sensors are grouped in areas and are used to define focal behaviors. These local behaviors are then gathered at the second level in order to define a global behavior. Two experiments with different local behaviors and different mechanism of integration are presented on our Nomad200 mobile robot.

SIRENE, a system for speech training of deaf people

This paper describes the SIRENE system which is being developed in our laboratory. SIRENE is an interactive computer-based system of speech-training aids for the deaf. It also includes a variety of procedures for analysis and classification of pathological voices. The basic idea of speech-training aids consists of compensating for the lack of auditory feedback in deaf children by use of visual displays. The system is intended to be used by speech teachers ; several acoustic and phonetic parameters of speech can be displayed and trained : pitch, voicing, intensity, etc... SIRENE also features the use of automatic speech recognition algorithms in the training of sounds and words.

Speaker time-drifting adaptation using trajectory mixture hidden Markov models

In this paper, a trajectory mixture hidden Markov model (TMHMM) is proposed to cope with the problems of trajectory variability and speaker time-drifting. A theoretical formulation of TMHMM learning is presented. By introducing two pragmatic adaptation schemes, the practical issues which demonstrate the use of the model in capturing the time-drifting of speaker model for speaker recognition are addressed. To evaluate with the YOHO corpus, a set of phonetic units is defined. The effectiveness of the modeling approach is confirmed by a set of experiments. It is shown that an error rate of 0.07% is obtained for closed-set speaker recognition with a total population of 138 talkers. TMHMM can be considered as a special HMM topology dedicated to the time-drifting adaptation problem.

Syntactic--Semantic interpretation of sentences in the MYRTILLE II speech understanding system

MYRTILLE II is a system which is now being implemented at CRIN in order to understand a wide subset of natural spoken French. In this paper we will concentrate on the syntactic-semantic component of the system. The general principle and overall architecture of MYRTILLE II are first briefly described. The way in which syntax and semantics are integrated in a unified representation is then presented. This representation is equivalent to a network with procedural nodes. Several examples of processed sentences are finally discussed. The system yields a tree structure of an input sentence. The examples presented show how syntax and semantics are used for solving typical problems which occur in speech understanding.

Mask Estimation for Missing Data Recognition using Background Noise Sniffing

This paper addresses the problem of spectrographic mask estimation in the context of missing data recognition. At the difference of other denoising methods, missing data recognition does not match the whole spectrum with the acoustic models, but rather considers that some time-frequency pixels are missing, i.e. corrupted by noise. Correctly estimating these masks is very important for missing data recognizers. We propose a new approach that exploits some a priori knowledge about these masks in typical noisy environments to address this difficult challenge. The proposed mask is then obtained by combining these noise dependent masks. The combination is led by an environmental sniffing module that estimates the probability of being in each typical noisy condition. This missing data mask estimation procedure has been integrated in a complete missing data recognizer using bounded marginalization. Our approach is evaluated on the Auroral database

Minimum cross-entropy adaptation of hidden Markov models

Adaptation techniques that benefit from distribution correlation are important in practical situations having sparse adaptation data. The so called extended MAP (EMAP) algorithm provides an optimal, though expensive, solution. In this article we start from EMAP, and propose an approximate optimisation criterion, based on maximising a set of local densities. We then obtain expressions for these local densities based on the principle of minimum cross-entropy (MCE). The solution to the MCE problem is obtained using an analogy with MAP estimation, and avoids the use of complex numerical procedures, thus resulting in a simple adaptation algorithm. The implementation of the proposed method for the adaptation of HMMs with mixture Gaussian densities is discussed, and its efficiency is evaluated on an alphabet recognition task.