Integrated approaches to prosodic word prediction for Chinese TTS

Guohong Fu; K. K. Luke

doi:10.1109/NLPKE.2003.1275941

Integrated approaches to prosodic word prediction for Chinese TTS

Guohong Fu, K. K. Luke

The University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This paper focuses on integrated prosodie word prediction for Chinese TTS, To avoid the problem of inconsistency between lexical words and prosodie words in Chinese, lexical word segmentation and prosodie word prediction are taken as one process instead of two independent tasks. Furthermore, two word-based approaches are proposed to drive this integrated prosodie word prediction: The first one follows the notion of lexicalized hidden Markov models, and the second one is borrowed from unknown word identification for Chinese. The results of our primary experiment show these integrated approaches are effective.

Original language	English
Title of host publication	NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings
Editors	Chengqing Zong
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	413-418
Number of pages	6
ISBN (Electronic)	0780379020, 9780780379022
DOIs	https://doi.org/10.1109/NLPKE.2003.1275941
Publication status	Published - 2003
Externally published	Yes
Event	International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2003 - Beijing, China Duration: Oct 26 2003 → Oct 29 2003

Publication series

Name	NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings

Conference

Conference	International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2003
Country/Territory	China
City	Beijing
Period	10/26/03 → 10/29/03

Bibliographical note

Publisher Copyright:
© 2003 IEEE.

ASJC Scopus Subject Areas

Artificial Intelligence
Computational Theory and Mathematics
Software

Keywords

Lexicalized HMMs
Prosodie word prediction
Text-to-speech synthesis

Access to Document

10.1109/NLPKE.2003.1275941

Cite this

Fu, G., & Luke, K. K. (2003). Integrated approaches to prosodic word prediction for Chinese TTS. In C. Zong (Ed.), NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings (pp. 413-418). Article 1275941 (NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NLPKE.2003.1275941

Fu, Guohong ; Luke, K. K. / Integrated approaches to prosodic word prediction for Chinese TTS. NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings. editor / Chengqing Zong. Institute of Electrical and Electronics Engineers Inc., 2003. pp. 413-418 (NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings).

@inproceedings{680735bc6b8242e3b48a078cc8be62a1,

title = "Integrated approaches to prosodic word prediction for Chinese TTS",

abstract = "This paper focuses on integrated prosodie word prediction for Chinese TTS, To avoid the problem of inconsistency between lexical words and prosodie words in Chinese, lexical word segmentation and prosodie word prediction are taken as one process instead of two independent tasks. Furthermore, two word-based approaches are proposed to drive this integrated prosodie word prediction: The first one follows the notion of lexicalized hidden Markov models, and the second one is borrowed from unknown word identification for Chinese. The results of our primary experiment show these integrated approaches are effective.",

keywords = "Lexicalized HMMs, Prosodie word prediction, Text-to-speech synthesis",

author = "Guohong Fu and Luke, {K. K.}",

note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2003 ; Conference date: 26-10-2003 Through 29-10-2003",

year = "2003",

doi = "10.1109/NLPKE.2003.1275941",

language = "English",

series = "NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "413--418",

editor = "Chengqing Zong",

booktitle = "NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings",

address = "United States",

}

Fu, G & Luke, KK 2003, Integrated approaches to prosodic word prediction for Chinese TTS. in C Zong (ed.), NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings., 1275941, NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 413-418, International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2003, Beijing, China, 10/26/03. https://doi.org/10.1109/NLPKE.2003.1275941

Integrated approaches to prosodic word prediction for Chinese TTS. / Fu, Guohong; Luke, K. K.
NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings. ed. / Chengqing Zong. Institute of Electrical and Electronics Engineers Inc., 2003. p. 413-418 1275941 (NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Integrated approaches to prosodic word prediction for Chinese TTS

AU - Fu, Guohong

AU - Luke, K. K.

PY - 2003

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N2 - This paper focuses on integrated prosodie word prediction for Chinese TTS, To avoid the problem of inconsistency between lexical words and prosodie words in Chinese, lexical word segmentation and prosodie word prediction are taken as one process instead of two independent tasks. Furthermore, two word-based approaches are proposed to drive this integrated prosodie word prediction: The first one follows the notion of lexicalized hidden Markov models, and the second one is borrowed from unknown word identification for Chinese. The results of our primary experiment show these integrated approaches are effective.

AB - This paper focuses on integrated prosodie word prediction for Chinese TTS, To avoid the problem of inconsistency between lexical words and prosodie words in Chinese, lexical word segmentation and prosodie word prediction are taken as one process instead of two independent tasks. Furthermore, two word-based approaches are proposed to drive this integrated prosodie word prediction: The first one follows the notion of lexicalized hidden Markov models, and the second one is borrowed from unknown word identification for Chinese. The results of our primary experiment show these integrated approaches are effective.

KW - Lexicalized HMMs

KW - Prosodie word prediction

KW - Text-to-speech synthesis

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DO - 10.1109/NLPKE.2003.1275941

M3 - Conference contribution

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T3 - NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings

SP - 413

EP - 418

BT - NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings

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PB - Institute of Electrical and Electronics Engineers Inc.

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Fu G, Luke KK. Integrated approaches to prosodic word prediction for Chinese TTS. In Zong C, editor, NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2003. p. 413-418. 1275941. (NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings). doi: 10.1109/NLPKE.2003.1275941

Integrated approaches to prosodic word prediction for Chinese TTS

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this