Robust integration for speech features

Deployment of speech recognizers sometimes involves ambient conditions that are not present during the training phase of the operation. This environment mismatch is one major source of performance degradation for speech recognizers. This paper aims at the feature integration that is robust to the ambient environment change. Our consideration focuses on the speech cepstrum and the group delay spectrum (GDS), derived from linear prediction coefficients. We present a new feature integration approach for robust speech recognition in adverse conditions. Based on the robustness of cepstrum under clean environment and the robustness of GDS under noisy environment, it is effective by suitably combining the cepstral coefficient and the GDS coefficient for noise-resistance of speech in different condition mismatch. The performance of the proposed method is experimentally evaluated in speaker-independent isolated-word recognition task using the hidden Markov model (HMM) under various noise conditions. Noisy speech is simulated by adding noise sources taken from the NOISEX-92 database. Experimental results obtained show that the new robust feature is effective for the speech recognition with significant noise and yields better performance than other feature coefficients. A substantial increase in recognition accuracy is observed in all testing noise environments at all different SNRs.