Discriminative Training for Speech Recognition is Compensating for Statistical Dependence on the HMM Framework

TitleDiscriminative Training for Speech Recognition is Compensating for Statistical Dependence on the HMM Framework
Publication TypeConference Paper
Year of Publication2012
AuthorsGillick, D., Wegmann S., & Gillick L.
Page(s)4745-4748
Other Numbers3240
Abstract

The parameters of the standard Hidden Markov Model framework for speech recognition are typically trained via Maximum Likelihood. However, better recognition performance is achievable with discriminative training criteria like Maximum Mutual Information or Minimum Phone Error. While it is generally accepted that these discriminative criteria are better suited to minimizing Word Error Rate, there is very little qualitative intuition for how the improvements are achieved. Through a series of “resampling” experiments, we show that discriminative training (MPE in particular) appears to be compensating for a specific incorrect assumption of the HMM-that speech frames are conditionally independent.

Acknowledgment

This work was partially supported by funding provided to ICSI through National Science Foundation grant IIS-1015930 (“Exploratory Data Analysis for Speech Recognition”). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors or originators and do not necessarily reflect the views of the National Science Foundation.

URLhttp://www.icsi.berkeley.edu/pubs/speech/gillickwegmannicassp12.pdf
Bibliographic Notes

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), pp. 4745-4748, Kyoto, Japan

Abbreviated Authors

D. Gillick, S. Wegmann, and L. Gillick

ICSI Research Group

Speech

ICSI Publication Type

Article in conference proceedings