Investigating Data Selection for Minimum Phone Error Training of Acoustic Models
Shih‐Hung Liu, Fang-Hui Chu, Shih-Hsiang Lin, Berlin Chen · 2007
This paper considers minimum phone error (MPE) based discriminative training of acoustic models for Mandarin broadcast news recognition. A novel data selection approach based on the normalized frame-level entropy of Gaussian posterior probabilities obtained from the word lattice of the training utterance was explored. It has the merit of making the training algorithm focus much more on the training statistics of those frame samples that center nearly around the decision boundary for better discrimination. Moreover, we presented a new phone accuracy function based on the frame-level accuracy of hypothesized phone arcs instead of using the raw phone accuracy function of MPE training. The underlying characteristics of the presented approaches were extensively investigated and their performance was verified by comparison with the original MPE training approach. Experiments conducted on the broadcast news collected in Taiwan showed that the integration of the frame-level data selection and accuracy calculation could achieve slight but consistent improvements over the baseline system.