Automatic phonetic segmentation of Malay speech database

This paper deals with automatic phonetic segmentation for Malay continuous speech. This study investigates fast and automatic phone segmentation in preparing database for Malay concatenative Text-to-Speech (TTS) systems. A 35 Malay phone set has been chosen, which is suitable for building Malay TTS. The segmentation experiment is based on this phone set. HMM based segmentation approach which uses Viterbi force alignment technique is adapted. We use continuous density HMM (CDHMM) with Gaussian mixture which is performs well in speech recognition to prevent large segmentation errors. Besides, this paper presents an implicit boundary refinement method that is incorporated in the Viterbi phonetic alignment. In this approach, the HMM model is trained with phone tokens with their boundaries extended to the be-side phones. This increases the ability of the HMM in modeling phone boundaries and provides effect of implicit boundary refinement when used in phonetic alignment thus reduce segmentation errors. This approach improves increase the performance of baseline HMM segmentation from 42.39%, 74.83%, 84.34% of automatic boundary marks within error smaller than 5, 15, and 25ms to 47.75%, 76.38%, 85.55%.