Multi-tape finite-state transducer for asynchronous multi-stream pattern recognition with application to speech

In this thesis, we have focused on improving the acoustic modeling of speech recognition systems to increase the overall recognition performance. We formulate a novel multi-stream speech recognition framework using multi-tape finite-state transducers (FSTs). The multi-dimensional input labels of the multi-tape FST transitions specify the acoustic models to be used for the individual feature streams. An additional auxiliary field is used to model the degree of asynchrony among the feature streams. The individual feature streams can be linear sequences such as fixed-frame-rate features in traditional hidden Markov model (HMM) systems, and the feature streams can also be directed acyclic graphs such as segment features in segment-based systems. In a single-tape mode, this multi-stream framework also unifies the frame-based HMM and the segment-based approach. Systems using the multi-stream speech recognition framework were evaluated on an audio-only and an audio-visual speech recognition task. On the Wall Street Journal speech recognition task, the multi-stream framework combined a traditional frame-based HMM with segment-based landmark features.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Shu, Han, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• James R. Glass.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/37880

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/37880