It is well known that a higher-than-normal speech rate will cause the rate of recognition errors in large vocabulary automatic speech recognition (ASR) systems to increase [1]. In this paper we attempt to identify and correct for errors due to fast speech. We first suggest that phone rate is a more meaningful measure of speech rate than the more common word rate. We find that when data sets are clustered according to the phone rate metric, recogni tion errors increase when the phone rate is more than 1 standard deviation greater than the mean. We propose three methods to improve the recognition accuracy of fast speech, each addressing different aspects of performance degradation. The first method is an implementation of Baum-Welch codebook adaptation. The sec ond method is based on the adaptation of HMM state-transition probabilities. In the third method, the pronunciation dictionaries are modified using rule-based techniques and compound words are added. We compare improvements in recognition accuracy for each method using data sets clustered according to the phone rate metric. Adaptation of the HMM state-transition probabilities to fast speech improves recognition of fast speech by a relative amount of 4 to 6 percent.
Mathew Siegler and Richard M.Stern REF proposed a work in which the errors due to fast speech was identified and corrected using the methods based on Baum Welch codebook adaptation, HMM state transition probability and rule based techniques.
On the effects of speech rate in large vocabulary speech recognition systems
1995 International Conference on Acoustics, Speech, and Signal Processing
1995 International Conference on Acoustics, Speech, and Signal Processing
Computer Science