A hybrid SVM/DDBHMM decision fusion modeling for robust continuous digital speech recognition

Abstract

This paper proposes an improved hybrid support vector machine and duration distribution based hidden Markov (SVM/DDBHMM) decision fusion model for robust continuous digital speech recognition. We investigate the probability outputs combination of support vector machine and Gaussian mixture model in pattern recognition ( called FSVM),and embed the fusion probability as similarity into the phone state level decision space of our duration distribution based hidden Markov model (DDBHMM) speech recognition system ( named FSVM/DDBHMM). The performances of FSVM and FSVM/DDBHMM are demonstrated in Iris database and continuous mandarin digital speech corpus in 4 noise environments (white, volvo, babble and destroyerengine) from NOISEX-92. The experimental results show the effectiveness of FSVM in Iris data, and the improvement of average word error rate reduction of FSVM/DDBHMM from 6% to 20% compared with the DDBHMM baseline at various signal noise ratios (SNRs) from −5 dB to 30 dB by step of 5 dB.