Closed-loop auditory-based representation for robust speech recognition

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.

Bibliographic Details
Main Author: Lee, Chia-ying (Chia-ying Jackie)
Other Authors: James R. Glass and Oded Ghitza.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2010
Subjects:
Online Access:http://hdl.handle.net/1721.1/60176
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author Lee, Chia-ying (Chia-ying Jackie)
author2 James R. Glass and Oded Ghitza.
author_facet James R. Glass and Oded Ghitza.
Lee, Chia-ying (Chia-ying Jackie)
author_sort Lee, Chia-ying (Chia-ying Jackie)
collection MIT
description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
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spelling mit-1721.1/601762019-04-12T16:01:59Z Closed-loop auditory-based representation for robust speech recognition Lee, Chia-ying (Chia-ying Jackie) James R. Glass and Oded Ghitza. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. Includes bibliographical references (p. 93-96). A closed-loop auditory based speech feature extraction algorithm is presented to address the problem of unseen noise for robust speech recognition. This closed-loop model is inspired by the possible role of the medial olivocochlear (MOC) efferent system of the human auditory periphery, which has been suggested in [6, 13, 42] to be important for human speech intelligibility in noisy environment. We propose that instead of using a fixed filter bank, the filters used in a feature extraction algorithm should be more flexible to adapt dynamically to different types of background noise. Therefore, in the closed-loop model, a feedback mechanism is designed to regulate the operating points of filters in the filter bank based on the background noise. The model is tested on a dataset created from TIDigits database. In this dataset, five kinds of noise are added to synthesize noisy speech. Compared with the standard MFCC extraction algorithm, the proposed closed-loop form of feature extraction algorithm provides 9.7%, 9.1% and 11.4% absolution word error rate reduction on average for three kinds of filter banks respectively. by Chia-ying Lee. S.M. 2010-12-06T17:33:49Z 2010-12-06T17:33:49Z 2010 2010 Thesis http://hdl.handle.net/1721.1/60176 681901837 eng 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. http://dspace.mit.edu/handle/1721.1/7582 96 p. application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Lee, Chia-ying (Chia-ying Jackie)
Closed-loop auditory-based representation for robust speech recognition
title Closed-loop auditory-based representation for robust speech recognition
title_full Closed-loop auditory-based representation for robust speech recognition
title_fullStr Closed-loop auditory-based representation for robust speech recognition
title_full_unstemmed Closed-loop auditory-based representation for robust speech recognition
title_short Closed-loop auditory-based representation for robust speech recognition
title_sort closed loop auditory based representation for robust speech recognition
topic Electrical Engineering and Computer Science.
url http://hdl.handle.net/1721.1/60176
work_keys_str_mv AT leechiayingchiayingjackie closedloopauditorybasedrepresentationforrobustspeechrecognition