Towards robustness to fast speech in ASR

Psychoacoustic studies show that human listeners are sensitive to speaking rate variations. Automatic speech recognition (ASR) systems are even more affected by the changes in rate, as double to quadruple word recognition error rates of average speakers have been observed for fast speakers on many ASR systems. In our earlier work (see Proceedings of EUROSPEECH95, p.491-4, 1995), we studied the causes of higher error and concluded that both the acoustic-phonetic and the phonological differences are sources of higher word error rates. In this work, we have studied various measures for quantifying rate of speech (ROS) and used simple methods for estimating the speaking rate of a novel utterance using ASR technology. We have also implemented mechanisms that make our ASR system more robust to fast speech. Using our ROS estimator to identify fast sentences in the test set, our rate-dependent system has 24.5% fewer errors on the fastest sentences and 6.2% fewer errors on all sentences of the WSJ93 evaluation set relative to the baseline HMM/MLP system.