Analysis of acoustic models trained on a large-scale Japanese speech database

Tomoko Matsui, Masaki Naito, Yoshinori Sagisaka, Kozo Okuda, Satoshi Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper investigates the performance of speaker-independent (SI) acoustic hidden-Markov-models (HMMs) trained with a huge Japanese speech database, and discusses the efficiency and task-independency involved. The database consists of read and spontaneous speech uttered by 3,771 speakers. The speech involves wide distributions with respect to region and age to capture the Japanese speech characteristics as best as possible. Recognition experiments using the spontaneous speech show that task-independent acoustic models can be created when training data with a huge number of speakers is available.

Original languageEnglish
Title of host publication6th International Conference on Spoken Language Processing, ICSLP 2000
PublisherInternational Speech Communication Association
ISBN (Electronic)7801501144, 9787801501141
Publication statusPublished - 2000
Externally publishedYes
Event6th International Conference on Spoken Language Processing, ICSLP 2000 - Beijing, China
Duration: 2000 Oct 162000 Oct 20

Other

Other6th International Conference on Spoken Language Processing, ICSLP 2000
CountryChina
CityBeijing
Period00/10/1600/10/20

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics

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  • Cite this

    Matsui, T., Naito, M., Sagisaka, Y., Okuda, K., & Nakamura, S. (2000). Analysis of acoustic models trained on a large-scale Japanese speech database. In 6th International Conference on Spoken Language Processing, ICSLP 2000 International Speech Communication Association.