A one-step tone recognition approach using MSD-HMM for continuous speech

There are two types of methods for tone recognition of continuous speech: one-step and two-step approaches. Two-step approaches need to identify the syllable boundaries firstly, while one-step approaches do not. Previous studies mostly focus on two-step approaches. In this paper, a one-step approach using Multi-space distribution HMM (MSD-HMM) is investigated. The F0, which only exists in voiced speech, is modeled by MSD-HMM. Then, a tonal syllable network is built based on the reference and Viterbi search is carried out on it to find the best tone sequence. Two modifications to the conventional triphone HMM models are investigated: tone-based context expansion and syllable-based model units. The experimental results proved that tone-based context information is more important for tone recognition and syllable-based HMM models are much better than phone-based ones. The final tone correct rate result is 88.8%, which is much higher than the state-of-the-art two-step approaches.