Automatic Segmentation and Labeling of Speech Corpus Based on HMM with Adaptation

In this article we advise to adopt the adaptive technique of acoustic model in the automatic segmentation and labeling of speech corpus. Since the precision of the data segmentation only based on speaker independent model is not good enough, we should transform the speaker independent model into the speaker dependent one. The training method leading to speaker dependent model needs a large amount of training data and will cost a lot of time, while the adaptive method can modify model parameters to match current speaker in a short time with a few training data and get comparatively precise segmentation results. And at the same time, in order to make the segmentation results more precise, we also combine the boundary adjustment based on the features of acoustics and phonetics and adopt an iterative procedure.