We conducted experiments on forced alignment in Mandarin Chinese. A corpus of 7,849 utterances was created for the purpose of the study. Systems differing in their use of explicit phone boundary models, glottal features, and tone information were trained and evaluated on the corpus. Results showed that employing special one-state phone boundary HMM models significantly improved forced alignment accuracy, even when no manual phonetic segmentation was available for training. Spectral features extracted from glottal waveforms (by performing glottal inverse filtering from the speech waveforms) also improved forced alignment accuracy. Tone dependent models only slightly outperformed tone independent models. The best system achieved 93.1% agreement (of phone boundaries) within 20 ms compared to manual segmentation without boundary correction.
