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A general discriminative training algorithm for speech recognition using weighted finite-state transducers

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4 Author(s)
Yong Zhao ; Dept. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA ; Ljolje, A. ; Caseiro, D. ; Biing-Hwang Juang

In this paper, we present a general algorithmic framework based on WFSTs for implementing a variety of discriminative training methods, such as MMI, MCE, and MPE/MWE. In contrast to the ordinary word lattices, the transducer-based lattices are more amenable to representing and manipulating the underlying hypothesis space and have a finer granularity at the HMM-state level. The transducers are processed into a two-layer hierarchy: at a high level, it is analogous to a word lattice, and each word transition embodies an HMM-state subgraph for that word at a lower level. This hierarchy combined with the appropriate customization of the transducers leads to a flexible implementation for all of the training criteria being discussed. The effectiveness of the framework is verified on two speech recognition tasks: Resource Management, and AT&T SCANMail, an internal voicemail-to-text task.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Date of Conference:

25-30 March 2012