Speaker verification robust to talking style variation using multiple kernel learning based on conditional entropy minimization

研究成果: Conference article査読

1 被引用数 (Scopus)

抄録

We developed a new speaker verification system that is robust to intra-speaker variation. There is a strong likelihood that intra-speaker variations will occur due to changes in talking styles, the periods when an individual speaks, and so on. It is well known that such variation generally degrades the performance of speaker verification systems. To solve this problem, we applied multiple kernel learning (MKL) based on conditional entropy minimization, which impose the data to be compactly aggregated for each speaker class and ensure that the different speaker classes were far apart from each other. Experimental results showed that the proposed speaker verification system achieved a robust performance to intra-speaker variation derived from changes in the talking styles compared to the conventional maximum margin-based system.

本文言語English
ページ(範囲)2741-2744
ページ数4
ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版ステータスPublished - 2011 12 1
イベント12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
継続期間: 2011 8 272011 8 31

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

フィンガープリント 「Speaker verification robust to talking style variation using multiple kernel learning based on conditional entropy minimization」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