This report introduces an hybrid speech recognition system for Speaker Independent (SI), continuous speech with a small vocabulary (sequences of Italian digits). The hybrid is based on parallel, state-space recurrent neural networks trained to perform a-posteriori state probability estimates of an underlying hidden Markov model (HMM) with fixed transition probabilities, given the sequence of acoustic observations. Training is accomplished in a supervised manner, relying on a prior Viterbi segmentation. Decoding is accomplished in a Dynamic programming framework, i.e. transitions along paths of the HMM, realizing a Viterbi decoding criterion. Preliminary experimental results of state-emission probability estimation and recognition of noisy signals acquired on the telephone line are presented

Integrating Recurrent Neural Networks and Dynamic Programming in an Hybrid Speech Recognition System: Concepts and Preliminary Results

Trentin, Edmondo
1996-01-01

Abstract

This report introduces an hybrid speech recognition system for Speaker Independent (SI), continuous speech with a small vocabulary (sequences of Italian digits). The hybrid is based on parallel, state-space recurrent neural networks trained to perform a-posteriori state probability estimates of an underlying hidden Markov model (HMM) with fixed transition probabilities, given the sequence of acoustic observations. Training is accomplished in a supervised manner, relying on a prior Viterbi segmentation. Decoding is accomplished in a Dynamic programming framework, i.e. transitions along paths of the HMM, realizing a Viterbi decoding criterion. Preliminary experimental results of state-emission probability estimation and recognition of noisy signals acquired on the telephone line are presented
1996
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/1299
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