Aspect-model-based reference speaker weighting

Seongjun Hahm, Yuichi Ohkawa, Masashi Ito, Motoyuki Suzuki, Akinori Ito, Shozo Makino

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

We propose an aspect-model-based reference speaker weighting. The main idea of the approach is that the adapted model is a linear combination of a set of reference speakers like reference speaker weighting (RSW) and eigenvoices. The aspect model is the mixture model of speaker-dependent (SD) models. In this paper, aspect model weighting (AMW) is proposed for finding an optimal weighting of a set of reference speakers unlike RSW and the aspect model which is a kind of cluster models is trained based on likelihood maximization with respect to the training data. The number of adaptation parameters can also be reduced using aspect model approach. For evaluation, we carried out an isolated word recognition experiment on Korean database (KLE452). The results were compared to those of conventional MAP, MLLR, RSW, and eigenvoice. Even though we use only 0.5s of adaptation data, 27.24% relative error rate reduction in comparison with speaker-independent (SI) baseline performance was achieved.

本文言語English
ホスト出版物のタイトル2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
ページ4302-4305
ページ数4
DOI
出版ステータスPublished - 2010 11 8
イベント2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
継続期間: 2010 3 142010 3 19

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period10/3/1410/3/19

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

フィンガープリント 「Aspect-model-based reference speaker weighting」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル