Model parameter estimation for mixture density polynomial segment models

Toshiaki Fukada*, Yoshinori Sagisaka, Kuldip K. Paliwal

*この研究の対応する著者

研究成果: Conference article査読

22 被引用数 (Scopus)

抄録

In this paper, we propose parameter estimation techniques for mixture density polynomial segment models (henceforth MDPSM) where their trajectories are specified with an arbitrary regression order. MDPSM parameters can be trained in one of three different ways: (1) segment clustering, (2) expectation maximization (EM) training of mean trajectories, or (3) EM training of mean and variance trajectories. These parameter estimation methods were evaluated in TIMIT vowel classification experiments. The experimental results showed that modeling both the mean and variance trajectories are consistently superior to modeling only the mean trajectory. We also found that modeling both trajectories results in significant improvements over the conventional HMM.

本文言語English
ページ(範囲)1403-1406
ページ数4
ジャーナルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2
出版ステータスPublished - 1997 1月 1
外部発表はい
イベントProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger
継続期間: 1997 4月 211997 4月 24

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

フィンガープリント

「Model parameter estimation for mixture density polynomial segment models」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