An Improved Greedy Search Algorithm for the Development of a Phonetically Rich Speech Corpus

Jin-Song ZHANG; Satoshi NAKAMURA

doi:10.1093/ietisy/e91-d.3.615

An Improved Greedy Search Algorithm for the Development of a Phonetically Rich Speech Corpus

Jin-Song ZHANG, Satoshi NAKAMURA

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An efficient way to develop large scale speech corpora is to collect phonetically rich ones that have high coverage of phonetic contextual units. The sentence set, usually called as the minimum set, should have small text size in order to reduce the collection cost. It can be selected by a greedy search algorithm from a large mother text corpus. With the inclusion of more and more phonetic contextual effects, the number of different phonetic contextual units increased dramatically, making the search not a trivial issue. In order to improve the search efficiency, we previously proposed a so-called least-to-most-ordered greedy search based on the conventional algorithms. This paper evaluated these algorithms in order to show their different characteristics. The experimental results showed that the least-to-most-ordered methods successfully achieved smaller objective sets at significantly less computation time, when compared with the conventional ones. This algorithm has already been applied to the development a number of speech corpora, including a large scale phonetically rich Chinese speech corpus ATRPTH which played an important role in developing our multi-language translation system.

Publication: IEICE TRANSACTIONS on Information Vol.E91-D No.3 pp.615-630

Publication Date: 2008/03/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e91-d.3.615

Type of Manuscript: Special Section PAPER (Special Section on Robust Speech Processing in Realistic Environments)

Category: Corpus

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Jin-Song ZHANG, Satoshi NAKAMURA, "An Improved Greedy Search Algorithm for the Development of a Phonetically Rich Speech Corpus" in IEICE TRANSACTIONS on Information, vol. E91-D, no. 3, pp. 615-630, March 2008, doi: 10.1093/ietisy/e91-d.3.615.
Abstract: An efficient way to develop large scale speech corpora is to collect phonetically rich ones that have high coverage of phonetic contextual units. The sentence set, usually called as the minimum set, should have small text size in order to reduce the collection cost. It can be selected by a greedy search algorithm from a large mother text corpus. With the inclusion of more and more phonetic contextual effects, the number of different phonetic contextual units increased dramatically, making the search not a trivial issue. In order to improve the search efficiency, we previously proposed a so-called least-to-most-ordered greedy search based on the conventional algorithms. This paper evaluated these algorithms in order to show their different characteristics. The experimental results showed that the least-to-most-ordered methods successfully achieved smaller objective sets at significantly less computation time, when compared with the conventional ones. This algorithm has already been applied to the development a number of speech corpora, including a large scale phonetically rich Chinese speech corpus ATRPTH which played an important role in developing our multi-language translation system.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.3.615/_p

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@ARTICLE{e91-d_3_615,
author={Jin-Song ZHANG, Satoshi NAKAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={An Improved Greedy Search Algorithm for the Development of a Phonetically Rich Speech Corpus},
year={2008},
volume={E91-D},
number={3},
pages={615-630},
abstract={An efficient way to develop large scale speech corpora is to collect phonetically rich ones that have high coverage of phonetic contextual units. The sentence set, usually called as the minimum set, should have small text size in order to reduce the collection cost. It can be selected by a greedy search algorithm from a large mother text corpus. With the inclusion of more and more phonetic contextual effects, the number of different phonetic contextual units increased dramatically, making the search not a trivial issue. In order to improve the search efficiency, we previously proposed a so-called least-to-most-ordered greedy search based on the conventional algorithms. This paper evaluated these algorithms in order to show their different characteristics. The experimental results showed that the least-to-most-ordered methods successfully achieved smaller objective sets at significantly less computation time, when compared with the conventional ones. This algorithm has already been applied to the development a number of speech corpora, including a large scale phonetically rich Chinese speech corpus ATRPTH which played an important role in developing our multi-language translation system.},
keywords={},
doi={10.1093/ietisy/e91-d.3.615},
ISSN={1745-1361},
month={March},}

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TY - JOUR
TI - An Improved Greedy Search Algorithm for the Development of a Phonetically Rich Speech Corpus
T2 - IEICE TRANSACTIONS on Information
SP - 615
EP - 630
AU - Jin-Song ZHANG
AU - Satoshi NAKAMURA
PY - 2008
DO - 10.1093/ietisy/e91-d.3.615
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2008
AB - An efficient way to develop large scale speech corpora is to collect phonetically rich ones that have high coverage of phonetic contextual units. The sentence set, usually called as the minimum set, should have small text size in order to reduce the collection cost. It can be selected by a greedy search algorithm from a large mother text corpus. With the inclusion of more and more phonetic contextual effects, the number of different phonetic contextual units increased dramatically, making the search not a trivial issue. In order to improve the search efficiency, we previously proposed a so-called least-to-most-ordered greedy search based on the conventional algorithms. This paper evaluated these algorithms in order to show their different characteristics. The experimental results showed that the least-to-most-ordered methods successfully achieved smaller objective sets at significantly less computation time, when compared with the conventional ones. This algorithm has already been applied to the development a number of speech corpora, including a large scale phonetically rich Chinese speech corpus ATRPTH which played an important role in developing our multi-language translation system.
ER -