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

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.