Rules, but what for ? - Rule description as efficient and robust abstraction of corpora and optimal fitting to applications-

Yoshinori Sagisaka, Hirofumi Yamamoto, Minoru Tsuzaki, Hiroaki Kato

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

Abstract

Two recent studies are introduced in speech recognition and speech synthesis to reconsider what rules should be looked for spoken language science and technology. To abstract the neighboring characteristics expressed by N-grams, multi-class composite N-grams have been proposed to model POS characteristics and inflectional forms separately. It is shown that statistical clustering can provide more compact and robust description of word neighboring characteristics than conventional N-grams. For speech synthesis, segmental duration modeling has been examined from the viewpoint of perceptual characteristics of duration changes. A series of perceptual experiments have shown the context dependency of sensitivity to duration change. These two examples respectively illustrate how current rules are interpreted to build scientifically acceptable engineering models and remind us of the deeper scientific meaning and limitation of generalization as a rule.

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

Fingerprint Dive into the research topics of 'Rules, but what for ? - Rule description as efficient and robust abstraction of corpora and optimal fitting to applications-'. Together they form a unique fingerprint.

Cite this