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A hybrid continuous speech recognition system using segmental neural nets with hidden Markov models

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4 Author(s)
S. Austin ; BBN Syst. & Technol., Cambridge, MA, USA ; G. Zavaliagkos ; J. Makhoul ; R. Schwartz

The authors present the concept of a `segmental neural net' (SNN) for phonetic modeling in continuous speech recognition (CSR) and demonstrate how than can be used with a multiple hypothesis (or N-Best) paradigm to combine different CSR systems. In particular, they have developed a system that combines the SNN with a hidden Markov model (HMM) system. They believe that this is the first system incorporating a neural network for which the performance has exceeded the state of the art in large-vocabulary, continuous speech recognition. To take advantage of the training and decoding speed of HMMs, the authors have developed a novel hybrid SNN/HMM system that combines the advantages of both types of approaches. In this hybrid system, use is made of the N-best paradigm to generate likely phonetic segmentations, which are then scored by the SNN. The HMM and SNN scores are then combined to optimize performance

Published in:

Neural Networks for Signal Processing [1991]., Proceedings of the 1991 IEEE Workshop

Date of Conference:

30 Sep-1 Oct 1991