A robust algorithm for word boundary detection in the presence of noise

The authors address the problem of automatic word boundary detection in quiet and in the presence of noise. Attention has been given to automatic word boundary detection for both additive noise and noise-induced changes in the talker's speech production (Lombard reflex). After a comparison of several automatic word boundary detection algorithms in different noisy-Lombard conditions, they propose a new algorithm that is robust in the presence of noise. This new algorithm identifies islands of reliability (essentially the portion of speech contained between the first and the last vowel) using time and frequency-based features and then, after a noise classification, applies a noise adaptive procedure to refine the boundaries. It is shown that this new algorithm outperforms the commonly used algorithm developed by Lamel (1981) et al. and several other recently developed methods. They evaluated the average recognition error rate due to word boundary detection in an HMM-based recognition system across several signal-to-noise ratios and noise conditions. The recognition error rate decreased to about 20% compared to an average of approximately 50% obtained with a modified version of the Lamel et al. algorithm