Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.

Bibliographic Details
Main Author: Chu, Karen Lingyun
Other Authors: Wade Shen and Robert C. Berwick.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2011
Subjects:
Online Access:http://hdl.handle.net/1721.1/61281
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author Chu, Karen Lingyun
author2 Wade Shen and Robert C. Berwick.
author_facet Wade Shen and Robert C. Berwick.
Chu, Karen Lingyun
author_sort Chu, Karen Lingyun
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description Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
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spelling mit-1721.1/612812019-04-12T11:53:18Z Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese Chu, Karen Lingyun Wade Shen and Robert C. Berwick. 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 (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 53-56). Tone plays a fundamental role in Mandarin Chinese, as it plays a lexical role in determining the meanings of words in spoken Mandarin. For example, these two sentences ... (I like horses) and ... (I like to scold) differ only in the tone carried by the last syllable. Thus, the inclusion of tone-related information through analysis of pitch data should improve the performance of automatic speech recognition (ASR) systems on Mandarin Chinese. The focus of this thesis is to improve the performance of a non-tonal automatic speech recognition (ASR) system on a Mandarin Chinese corpus by implementing modifications to the system code to incorporate pitch features. We compile and format a Mandarin Chinese broadcast new corpus for use with the ASR system, and implement a pitch feature extraction algorithm. Additionally, we investigate two algorithms for incorporating pitch features in Mandarin Chinese speech recognition. Firstly, we build and test a baseline tonal ASR system with embedded tone modeling by concatenating the cepstral and pitch feature vectors for use as the input to our phonetic model (a Hidden Markov Model, or HMM). We find that our embedded tone modeling algorithm does improve performance on Mandarin Chinese, showing that including tonal information is in fact contributive for Mandarin Chinese speech recognition. Secondly, we implement and test the effectiveness of HMM-based multistream models. by Karen Lingyun Chu. M.Eng. 2011-02-23T14:41:11Z 2011-02-23T14:41:11Z 2009 2009 Thesis http://hdl.handle.net/1721.1/61281 702369091 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 56 p. application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Chu, Karen Lingyun
Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese
title Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese
title_full Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese
title_fullStr Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese
title_full_unstemmed Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese
title_short Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese
title_sort incorporating pitch features for tone modeling in automatic recognition of mandarin chinese
topic Electrical Engineering and Computer Science.
url http://hdl.handle.net/1721.1/61281
work_keys_str_mv AT chukarenlingyun incorporatingpitchfeaturesfortonemodelinginautomaticrecognitionofmandarinchinese