Speech recognitionwith segmental conditional random fields: A summary of the JHU CLSP 2010 Summer Workshop

This paper summarizes the 2010 CLSP Summer Workshop on speech recognition at Johns Hopkins University. The key theme of the workshop was to improve on state-of-the-art speech recognition systems by using Segmental Conditional Random Fields (SCRFs) to integrate multiple types of information. This app...

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Main Authors: Zweig, G., Nguyen, P., Van Compernolle, D., Demuynck, K., Atlas, L., Clark, P., Sell, G., Wang, M., Sha, F., Hermansky, H., Karakos, D., Jansen, A., Thomas, S., Sivaram, G. S. V. S., Bowman, S., Kao, J.
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
CRF
Detectors
Feature extraction
Hidden Markov models
Segmental Conditional Random Field
Speech
Speech recognition
Training
Online Access:Request full text
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Summary:This paper summarizes the 2010 CLSP Summer Workshop on speech recognition at Johns Hopkins University. The key theme of the workshop was to improve on state-of-the-art speech recognition systems by using Segmental Conditional Random Fields (SCRFs) to integrate multiple types of information. This approach uses a state of-the-art baseline as a springboard from which to add a suite of novel features including ones derived from acoustic templates, deep neural net phoneme detections, duration models, modulation features, and whole word point-process models. The SCRF framework is able to appropriately weight these different information sources to produce significant gains on both die Broadcast News and Wall Street Journal tasks.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2011.5947490