Speaking style adaptation for spontaneous speech recognition using multiple-regression HMM

Yusuke Ijima, Takeshi Matsubara, Takashi Nose, Takao Kobayashi

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper describes a rapid model adaptation technique for spontaneous speech recognition. The proposed technique utilizes a multiple-regression hidden Markov model (MRHMM) and is based on a style estimation technique of speech. In the MRHMM, the mean vector of probability density function (pdf) is given by a function of a low-dimensional vector, called style vector, which corresponds to the intensity of expressivity of speaking style variation. The value of the style vector is estimated for every utterance of the input speech and the model adaptation is conducted by calculating new mean vectors of the pdf using the estimated style vector. The performance evaluation results using "Corpus of spontaneous Japanese (CSJ)" are shown under a condition in which the amount of model training and adaptation data is very small.

Original languageEnglish
Pages (from-to)552-555
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2009 Nov 26
Externally publishedYes
Event10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
Duration: 2009 Sep 62009 Sep 10

Keywords

  • Multiple-regression HMM
  • Speaking style
  • Spontaneous speech
  • Style estimation

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

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