Sequence-Based Pronunciation Modeling Using a Noisy-Channel Approach

摘要

Previous approaches to spontaneous speech recognition address the multiple pronunciation problem by modeling the alteration of the pronunciation on a phoneme to phoneme level. However, the phonetic transformation effects induced by the pronunciation of the whole sentence are not considered yet. In this paper we attempt to model the sequence-based pronunciation variation using a noisy-channel approach where the spontaneous phoneme sequence is considered as a "noisy" string and the goal is to recover the "clean" string of the word sequence. Hereby, the whole word sequence and its effect on the alternation of the phonemes will be taken into consideration. Moreover, the system not only learns the phoneme transformation but also the mapping from the phoneme to the word directly. In this preliminary study, first the phonemes will be recognized with the present recognition system and afterwards the pronunciation variation model based on the noisy-channel approach will map from the phoneme to the word level. Our experiments use Switchboard as spontaneous speech corpus. The results show that the proposed method improves the word accuracy consistently over the conventional recognition system. The best system achieves up to 38.9% relative improvement to the baseline speech recognition.