Investigations into the Crandem approach to word recognition

Authors:
Rohit Prabhavalkar;Preethi Jyothi;William Hartmann;Jeremy Morris;Eric Fosler-Lussier
Affiliations:
The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH
Venue:
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2010

Citing 4
Cited 0

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Abstract

We suggest improvements to a previously proposed framework for integrating Conditional Random Fields and Hidden Markov Models, dubbed a Crandem system (2009). The previous authors' work suggested that local label posteriors derived from the CRF were too low-entropy for use in word-level automatic speech recognition. As an alternative to the log posterior representation used in their system, we explore frame-level representations derived from the CRF feature functions. We also describe a weight normalization transformation that leads to increased entropy of the CRF posteriors. We report significant gains over the previous Crandem system on the Wall Street Journal word recognition task.