Phone-discriminating minimum classification error (p-MCE) training for phonetic recognition

In this paper, we report a study on performance comparisons of discriminative training methods for phone recognition using the TIMIT database. We propose a new method of phonediscriminating minimum classification error (P-MCE), which performs MCE training at the sub-string or phone level instead of at the traditional string level. Aiming at minimizing the phone recognition error rate, P-MCE nevertheless takes advantage of the well-known, efficient training routine derived from the conventional string-based MCE, using specially constructed one-best lists selected from phone lattices. Extensive investigations and comparisons are conducted between the PMCE and other discriminative training methods including maximum mutual information (MMI), minimum phone or word error (MPE/MWE), and the other two MCE methods. The P-MCE outperforms most of experimented approaches on the standard TIMIT database in terms of the continuous phonetic recognition accuracy. P-MCE achieves comparable results with the MPE method which also aims at reducing phone-level recognition errors.