Automatic Phonetic Segmentation Using the Kaldi Toolkit

摘要

In this paper we explore the possibilities of hidden Markov model based automatic phonetic segmentation with the Kaldi toolkit. We compare the Kaldi toolkit and the Hidden Markov Model Toolkit (HTK) in terms of segmentation accuracy. The well-tuned HTK-based phonetic segmentation framework was taken as the baseline and compared to a newly proposed segmentation framework built from the default examples and recipes available in the Kaldi repository. Since the segmentation accuracy of the HTK-based system was significantly higher than that of the Kaldi-based system, the default Kaldi setting was modified with respect to pause model topology, the way of generating phonetic questions for clustering, and the number of Gaussian mixtures used during modeling. The modified Kaldi-based system achieved results comparable to those obtained by HTK--slightly worse for small segmentation errors but better for gross segmentation errors. We also confirmed that, for both toolkits, the standard three-state left-to-right model topology was significantly outperformed by a modified five-state left-to-right topology, especially with respect to small segmentation errors.