Showing papers by "R. De Mori published in 1991"

High performance connected digit recognition using maximum mutual information estimation

Abstract: The authors describe the latest development by the speech research group at CRIM (Centre de Recherche Informatique de Montreal) in speaker-independent connected digit recognition, using hidden Markov Models (HMMs) trained with maximum mutual information estimation, in conjunction with connectionist models. The experiments described were all done on the complete adult portion of the 10 kHz speaker-independent TI/NIST connected digit database. The baseline system, using discrete HMMs and maximum likelihood estimation, has a 98.6% word recognition rate and a 96.1% string recognition rate. The authors describe techniques that made it possible to improve greatly the baseline system recognition rate. The 99.3% recognition rate and 98.0% string recognition rate were obtained with a single model per unit using discrete HMMs and recurrent neural networks. Using semi-continuous HMMs with two models per digit (one for male and one for female speakers), a 99.5% word recognition rate and a 98.4% string recognition rate were achieved. >

Computation of probabilities for an island-driven parser

Abstract: The authors describe an effort to adapt island-driven parsers to handle stochastic context-free grammars. These grammars could be used as language models (LMs) by a language processor (LP) to computer the probability of a linguistic interpretation. As different islands may compete for growth, it is important to compute the probability that an LM generates a sentence containing islands and gaps between them. Algorithms for computing these probabilities are introduced. The complexity of these algorithms is analyzed both from theoretical and practical points of view. It is shown that the computation of probabilities in the presence of gaps of unknown length requires the impractical solution of a nonlinear system of equations, whereas the computation of probabilities for cases with gaps containing a known number of unknown words has polynomial time complexity and is practically feasible. The use of the results obtained in automatic speech understanding systems is discussed. >

Global optimization of a neural network-hidden Markov model hybrid

Abstract: An original method for integrating artificial neural networks (ANN) with hidden Markov models (HMM) is proposed. ANNs are suitable for performing phonetic classification, whereas HMMs have been proven successful at modeling the temporal structure of the speech signal. In the approach described, the ANN outputs constitute the sequence of observation vectors for the HMM. An algorithm is proposed for global optimization of all the parameters. Results on speaker-independent recognition experiments using this integrated ANN-HMM system on the TIMIT continuous speech database are reported. >

A probabilistic approach to person-robot dialogue

Abstract: An approach to designing dialogue systems, as applied to a robot with speech recognition capabilities, is described. One of the goals of this research is to develop probabilistic methods for dialogue systems analogous to those used in language modeling for dictation systems. These methods will use knowledge about dialogue structure, performance, syntax, and semantics to constrain the speech recognition task. >