Data selection for speech recognition

被引：36

作者：

Wu, Yi ^{[1
]}

Zhang, Rong ^{[1
]}

Rudnicky, Alexander ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA

来源：

2007 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING, VOLS 1 AND 2 | 2007年

关键词：

data selection; maximum entropy; speech recognition; acoustic modeling;

D O I：

10.1109/ASRU.2007.4430173

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a strategy for efficiently selecting informative data from large corpora of transcribed speech. We propose to choose data uniformly according to the distribution of some target speech unit (phoneme, word, character, etc). In our experiment, in contrast to the common belief that "there is no data like more data", we found it possible to select a highly informative subset of data that produces recognition performance comparable to a system that makes use of a much larger amount of data. At the same time, our selection process is efficient and fast.

引用

页码：562 / 565

页数：4

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