Cepstral domain segmental feature vector normalization for noise robust speech recognition

Abstract To date, speech recognition systems have been applied in real world applications in which they must be able to provide a satisfactory recognition performance under various noise conditions. However, a mismatch between the training and testing conditions often causes a drastic decrease in the performance of the systems. In this paper, we propose a segmental feature vector normalization technique which makes an automatic speech recognition system more robust to environmental changes by normalizing the output of the signal-processing front-end to have similar segmental parameter statistics in all noise conditions. The viability of the suggested technique was verified in various experiments using different background noises and microphones. In an isolated word recognition task, the proposed normalization technique reduced the error rates by over 70% in noisy conditions with respect to the baseline tests, and in a microphone mismatch case, over 75% error rate reduction was achieved. In a multi-environment speaker-independent connected digit recognition task, the proposed method reduced the error rates by over 16%.