Dealing with Limited and Noisy Data in ASR: a Hybrid Knowledge-based and Statistical Approach

被引：0

作者：

Alwan, Abeer ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Dept Elect Engn, Los Angeles, CA 90024 USA

来源：

INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5 | 2008年

关键词：

noise-robust ASR; speaker normalization; speaker adaptation; accented English; limited data; knowledge-based;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this talk, Twill focus on the importance of integrating knowledge of human speech production and speech perception mechanisms, and language-specific information with statistically-based, data-driven approaches to develop robust and scalable automatic speech recognition (ASR) systems. As we will demonstrate, the need for such hybrid systems is especially critical when the ASR system is dealing with noisy data, when adaptation data are limited (for the case of speaker normalization and adaptation), and when dealing with accents.

引用

页码：11 / 15

页数：5

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