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The Signal Processing and Pattern Recognition Group in Nancy is developing the MYRTILLE System for recognizing and understanding sentences in artificial and natural languages. A model for the oral control of a telephone exchange with an artificial language was proposed and described at ICASSP 76. In this paper, we first present a comparative study of two syntactic models that we have used concurrently in this experiment : a classical top-down model and a generalized bottom-up, island-driven model. Then, we discuss the generalization of this model to natural language understanding system (e.g. for the oral consultation of a data base). Such a generalization involves a very different structure, which is described in details.

The phonetic context has a large effect on stop consonants in a continuous speech signal. Therefore, recognition systems that model allophones using context-dependent hidden Markov models have been implemented (Lamel and Gauvain, 1993). HMMs have a great ability for segmentation in the temporal domain but have some difficulties in recognition because the MLE training (maximum likelihood estimation) is not discriminant, whereas discrimination is one of the abilities of artificial neural network models. In the last three years we have developed a new ANN model named OWE (Orthogonal Weight Estimator). The principle of the OWE is an ANN that classifies an input pattern according to the contextual environment. This new ANN architecture tackles the problem of context dependent behaviour training. Roughly, the principle is based on a main MLP (multilayered perceptron) in which each synaptic weight connection value is estimated by another MLP (an OWE) with respect to context representation. In this paper, we present a hierarchical system for phoneme recognition: first the system segments the input signal using 48 context independent HMMs. Then the stop consonants are reordered by an OWE ANN. Experiments on TIMIT show 78% correct recognition rate on the 6 stop consonants (/p, t, k, b, d, g).

Representation of speech using hidden Markov models is compared with a representation based on finite-state automata. Experimental results are presented for both methods and the respective advantages and disadvantages discussed. >

A recognizer for isolated words based on second order hidden Markov models (HMMs) is presented. This system is compared with the usual first order one. To evaluate the performances of both systems, a database of French digits is used. Results are more accurate with the recognizer using second-order hidden Markov process. A version of the Viterbi algorithm which is suited to continuous-speech recognition is described. This version, called Viterbi-block allows segmentation and labeling of an unknown utterance during its processing, whereas the classical Viterbi algorithm waits until the end of the sentence before backtracking the best path. This algorithm is based on a local optimum comparison of the different probabilities computed by the Viterbi algorithm during the time warping of a shift window of fixed length in the signal and the different HMMs. Different criteria of comparison between the probabilities of different paths are tried and assessed. >

Second order hidden Markov models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (neural networks etc.) are their capabilities of modeling noisy temporal signals of variable length. In a previous work, we proposed a new method based on second order hidden Markov models to learn and recognize places in an indoor environment by a mobile robot, and showed that this approach is well suited for learning and recognizing places. In this paper, we propose major modifications to increase the global rate of place recognition. Results of experiments on a real robot with distinctive places are given.

International audience; Automatic speech recognition and understanding (ASR) plays an important role in the framework of man-machine communication. Substantial industrial developments are at present in progress in this area. However, after 40 years or so of efforts several fundamental questions remain open. This paper is concerned with a comparative study of four different methods for multi-speaker word recognition: (i) clustering of acoustic templates, (ii) comparison with a finite state automaton, (iii) dynamic programming and vector quantization, (iv) stochastic Markov sources. In order to make things comparable, the four methods were tested with the same material made up of the ten digits (0 to 9) pronounced four times by 60 different speakers (30 males and 30 females). We will distinguish in our experiments between multi-speaker systems (capable of recognizing words pronounced by speakers that have been used during the training phase of the system) and speaker-independent systems (capable of recognizing words pronounced by speakers totally unknown to the system). Half of the corpus (15 male and 15 female) were used for training, and the remaining part for test.

In this paper, we propose a new method based on hidden Markov models to learn and recognize places in an indoor environment by a mobile robot. Hidden Markov models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (e.g. neural networks) are their capabilities to modelize noisy temporal signals of variable length. We show in this paper that this approach is well adapted for learning and recognition of places by a mobile robot. Results of experiments on a real robot with five distinctive places are given.