A phoneme duration model considering speaking‐rate and linguistic features for speech recognition

Abstract

In this paper, we proposed a method of phoneme duration modeling for speech recognition. A phoneme with extremely short or long duration often causes a decline of performance of speech recognition. In order to improve performance of recognition, an estimation of phoneme duration determined by various parameters is required. However, there was no usual method of duration modeling for speech recognition considering the influence of both speaking‐rate and linguistic feature (phoneme location in sentence, part‐of‐speech, et al.), which influence phoneme duration strongly. Therefore, we modeled influence of speaking‐rate by two‐dimensional normal distribution of phoneme duration and local average of vowel duration. Each normal distribution is determined by tree‐based clustering with various questions, which include linguistic feature. With an experiment of estimation of phoneme duration by this model, we acquired 20.8% reduction of standard deviation of estimation error. We also used the proposed duration model for rescoring of N‐best hypothesis of speech recognition. With an experiment of rescoring of recognition results for spontaneous speech, we acquired significant reduction of 4.7% in phoneme error rate.