Duration modeling techniques for continuous speech recognition

Phone durations play a significant part in the comprehension of speech. The duration information is still mostly disregarded in automatic speech recognizers due to the use of hidden Markov models (HMMs) which are deficient in modeling phone durations properly. Previous results have shown that using different approaches for explicit duration modeling have improved the isolated word recognition in English. However, a unified comparison between the methods has not been reported. In this paper three techniques for explicit duration modeling are compared and evaluated in a large vocabulary continuous speech recognition task. The target language was Finnish, in which phone durations are especially important for proper understanding. The results show that the choice of the duration modeling technique depends on the speed requirements of the recognizer. The best technique required a slightly longer running time than without an explicit duration model, but achieved an 8% relative improvement to the letter error rate.