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 phone-discriminating 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 P-MCE 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.
Bibliographic reference. Qian, Qian / He, Xiaodong / Deng, Li (2007): "Phone-discriminating minimum classification error (p-MCE) training for phonetic recognition", In INTERSPEECH-2007, 2073-2076.