Low-Resource Speech Recognition and Keyword-Spotting

被引：5
|
作者：
Gales, Mark J. F. [1 ]
Knill, Kate M. [1 ]
Ragni, Anton [1 ]
机构：
[1] Univ Cambridge, Dept Engn, Trumpington St, Cambridge, England
来源：
SPEECH AND COMPUTER, SPECOM 2017 | 2017年 / 10458卷
关键词：
Prosody perception; Narrow versus broad focus; Japanese learners of English; L2; acquisition; DEEP NEURAL-NETWORK; DATA AUGMENTATION;
D O I：
10.1007/978-3-319-66429-3_1
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
The IARPA Babel program ran from March 2012 to November 2016. The aim of the program was to develop agile and robust speech technology that can be rapidly applied to any human language in order to provide effective search capability on large quantities of real world data. This paper will describe some of the developments in speech recognition and keyword-spotting during the lifetime of the project. Two technical areas will be briefly discussed with a focus on techniques developed at Cambridge University: the application of deep learning for low-resource speech recognition; and efficient approaches for keyword spotting. Finally a brief analysis of the Babel speech language characteristics and language performance will be presented.
引用
收藏
页码：3 / 19
页数：17
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