Speaker-independent French digits recognition using word-based vector quantization and hidden Markov models

Vector Quantization has recently been used in the realization of a speaker-independent digit recognizer, based uniquely on the spectral content of the speech signal. On the other hand, the Hidden Markov Models proved their ability in modelling temporal distortions between different utterances of a word pronounced by several speakers. In term of recognition rate, HMMs are as efficient as the conventional DTW matching, but they need less computation and memory. This paper presents a speaker-independent digit recognition system that combines word-based VQ with HMM, the cost of which is low enough to be implemented on a single signal processor available today. It is the first result of a cooperation project between ENST and the MATRA company, financially supported by the French government. The proposed recognizer is structured in two parts. First, a VQ-preprocessor, with one vector codebook per vocabulary word, performs a coding of the short-time spectrum of the speech signal and realizes an initial sorting. Then HMMs are used to take the final recognition decision.