FASTWORD ACQUISITION IN AN NMF-BASED LEARNING FRAMEWORK

被引：0

作者：

Driesen, Joris ^{[1
]}

Van Hamme, Hugo ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Elect Engn ESAT, B-3001 Louvain, Belgium

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2012年

关键词：

Acoustic Sub-Word Generation; Unsupervised Learning; Vocabulary Acquisition; Machine Learning;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

A speech recognition system that automatically learns word models for a small vocabulary from examples of its usage, without using prior linguistic information, can be of great use in cognitive robotics, human-machine interfaces, and assistive devices. In the latter case, the user's speech capabilities may also be affected. In this paper, we consider a NMF-based learning framework capable of doing this, and experimentally show that its learning rate crucially depends on how the speech data is represented. Higher-level units of speech, which hide some of the complex variability of the acoustics, are found to yield faster learning rates.

引用

页码：5137 / 5140

页数：4

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