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

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概要

• 論文の詳細を見る

• 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.

• （社）電子情報通信学会の論文
• 2008-03-01

著者

• Zhang Jin‐song

  Spoken Language Communication Group Knowledge Creating Communication Research Center National Instit

• Zhang Jin-song

  Spoken Language Communication Group Knowledge Creating Communication Research Center National Instit

• NAKAMURA Satoshi

  Spoken Language Communication Group, Knowledge Creating Communication Research Center, National Inst

• Nakamura Satoshi

  Spoken Language Communication Group Knowledge Creating Communication Research Center National Institute Of Information And Communications Technology

関連論文

• An Improved Greedy Search Algorithm for the Development of a Phonetically Rich Speech Corpus
• Using Mutual Information Criterion to Design an Efficient Phoneme Set for Chinese Speech Recognition
• Automatic Generation of Non-uniform and Context-Dependent HMMs Based on the Variational Bayesian Approach(Feature Extraction and Acoustic Medelings, Corpus-Based Speech Technologies)
• Automatic Generation of Non-uniform HMM Topologies Based on the MDL Criterion(Speech and Hearing)
• Iterative mapping function estimation and environment structure refinement in the online phase of the ESSEM approach (音声)
• An Unsupervised Model of Redundancy for Answer Validation

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