Automatic allocation of training data for speech understanding based on multiple model combinations

Kazunori Komatani*, Mikio Nakano, Masaki Katsumaru, Kotaro Funakoshi, Tetsuya Ogata, Hiroshi G. Okuno

*この研究の対応する著者

研究成果査読

抄録

The optimal way to build speech understanding modules depends on the amount of training data available. When only a small amount of training data is available, effective allocation of the data is crucial to preventing overfitting of statistical methods. We have developed a method for allocating a limited amount of training data in accordance with the amount available. Our method exploits rule-based methods for when the amount of data is small, which are included in our speech understanding framework based on multiple model combinations, i.e., multiple automatic speech recognition (ASR) modules and multiple language understanding (LU) modules, and then allocates training data preferentially to the modules that dominate the overall performance of speech understanding. Experimental evaluation showed that our allocation method consistently outperforms baseline methods that use a single ASR module and a single LU module while the amount of training data increases.

本文言語English
ページ(範囲)2298-2307
ページ数10
ジャーナルIEICE Transactions on Information and Systems
E95-D
9
DOI
出版ステータスPublished - 2012 9
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

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