Date of Original Version

5-2013

Type

Conference Proceeding

Journal Title

Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing

First Page

7624

Last Page

7628

Rights Management

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Abstract or Description

This paper presents a novel approach to handling disfluencies, word fragments and self-interruption points in Cantonese conversational speech. We train a classifier that exploits lexical and acoustic information to automatically identify disfluencies during training of a speech recognition system on conversational speech, and then use this classifier to augment reference annotations used for acoustic model training. We experiment with approaches to modeling disfluencies in the pronunciation dictionary, and their effect on the polyphonic decision tree clustering. We achieve automatic detection of disfluencies with 88% accuracy, which leads to a reduction in character error rate of 1.9% absolute. While the high baseline error rates are due to the task we are currently working on, we demonstrate that this approach works well on the Switchboard corpus, for which the conversational nature of speech is also a major problem.

DOI

10.1109/ICASSP.2013.6639146

Comments

This paper presents a novel approach to handling disfluencies, word fragments and self-interruption points in Cantonese conversational speech. We train a classifier that exploits lexical and acoustic information to automatically identify disfluencies during training of a speech recognition system on conversational speech, and then use this classifier to augment reference annotations used for acoustic model training. We experiment with approaches to modeling disfluencies in the pronunciation dictionary, and their effect on the polyphonic decision tree clustering. We achieve automatic detection of disfluencies with 88% accuracy, which leads to a reduction in character error rate of 1.9% absolute. While the high baseline error rates are due to the task we are currently working on, we demonstrate that this approach works well on the Switchboard corpus, for which the conversational nature of speech is also a major problem.

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Published In

Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 7624-7628.