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James R. Glass and Oded Ghitza. Lee, Chia-ying (Chia-ying Jackie) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2010-12-06T17:33:49Z 2010-12-06T17:33:49Z 2010 2010 http://hdl.handle.net/1721.1/60176 681901837 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. 96 p. eng Massachusetts Institute of Technology 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 Electrical Engineering and Computer Science. Closed-loop auditory-based representation for robust speech recognition Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="86fca2b2-9a70-4150-9dbd-9c130388cf4c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Closed-loop auditory-based representation for robust speech recognition</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Lee, Chia-ying (Chia-ying Jackie)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>