Minimum boundary error training for automatic phonetic segmentation

Annotated speech corpora are indispensable to various areas of speech research. In this paper, we present a novel discriminative training approach for HMM-based automatic phonetic segmentation. The objective of the proposed minimum boundary error (MBE) discriminative training approach is to minimize the expected boundary errors over a set of phonetic alignments represented as a phonetic lattice. This approach is inspired by the recently proposed minimum phone error (MPE) training algorithm for automatic speech recognition. To evaluate the MBE training approach, we conducted automatic phonetic segmentation experiments on the TIMIT acoustic-phonetic continuous speech corpus. The MBE-trained HMMs can identify 79.75% of human-labeled phone boundaries within a tolerance of 10 ms, compared to 71.23% identified by the conventional ML-trained HMMs. Moreover, by using the MBE-trained HMMs, only 7.89% of automatically labeled phone boundaries have errors larger than 20 ms.