Out-of-vocabulary word recognition with a hierarchical doubly Markov language model

Hiroaki Kokubo, Hirofumi Yamamoto, Yoshihiko Ogawa, Yoshinori Sagisaka, Genichiro Kikui

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We describe a novel language model for task-dependent out-of-vocabulary (OOV) words. OOV words, such as personal names and place names in a new task can make the language model adaptation difficult. To cope with this problem, we propose a hierarchical, 2-layered language model consisting of inter-word constraints and intra-word constraints. Stochastic properties of OOV words in the two constraints are represented by multi-class modeling and trained as independent Markov models. Occurrence probabilities of an OOV word are expressed by statistics of two Markov Models (namely, doubly Markov model). The proposed model has been tested in a Japanese conversational speech database of appointment making. The word correct rate has been achieved 7.5% improvement from 78.2% to 86.7% when the new language model was used to recognize sentences with OOV words.

本文言語English
ホスト出版物のタイトル2003 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2003
出版社Institute of Electrical and Electronics Engineers Inc.
ページ543-547
ページ数5
ISBN(電子版)0780379802, 9780780379800
DOI
出版ステータスPublished - 2003
イベントIEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2003 - St. Thomas, United States
継続期間: 2003 11 302003 12 4

出版物シリーズ

名前2003 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2003

Other

OtherIEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2003
CountryUnited States
CitySt. Thomas
Period03/11/3003/12/4

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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