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Robust Speech Recognition for Unknown Communication ChannelsPont-à-Mousson, France |
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Robust Speech Recognition for Unknown Communication ChannelsPont-à-Mousson, France |
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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 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 over 70% in noisy conditions with respect to the baseline tests, and in a microphone mismatch case, over 75% error rate reduction was achieved.
Bibliographic reference. Viikki, Olli / Laurila, Kari (1997): "Noise robust HMM-based speech recognition using segmental cepstral feature vector normalization", In RSR-1997, 107-110.