Stream selection and integration in multistream ASR using GMM-based performance monitoring

Tetsuji Ogawa, Feipeng Li, Hynek Hermansky

研究成果: Conference article査読

5 被引用数 (Scopus)

抄録

A moderately deep and rather wide artificial neural net is applied in phoneme recognition of noisy speech. The net is formed by first estimating posterior probabilities of phonemes in 21 band-limited streams covering the whole speech spectrum. These 21 band-limited streams are subdivided into three seven band-limited stream subsets, by differently sub-sampling the original 21 band-limited streams. In the second processing stage, all non-empty combinations of seven band-limited streams from each subset are formed as inputs to 127 artificial neural nets that are again trained to yield phoneme posteriors. In this way, 127 × 3 = 381 processing streams are formed. A novel technique for finding the best combination of the resulting 381 parallel processing streams, which uses the likelihood of a single-state Gaussian mixture model of the final classifier output is applied to selecting the most efficient streams. The technique is efficient in phoneme recognition of speech that is corrupted by realistic additive noise.

本文言語English
ページ(範囲)3332-3336
ページ数5
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 - 2013 1 1
イベント14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013 - Lyon, France
継続期間: 2013 8 252013 8 29

ASJC Scopus subject areas

  • 言語および言語学
  • 人間とコンピュータの相互作用
  • 信号処理
  • ソフトウェア
  • モデリングとシミュレーション

フィンガープリント

「Stream selection and integration in multistream ASR using GMM-based performance monitoring」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