SMALL-FOOTPRINT KEYWORD SPOTTING USING DEEP NEURAL NETWORKS

被引：0

作者：

Chen, Guoguo ^{[1
]}

Parada, Carolina ^{[2
]}

Heigold, Georg ^{[2
]}

机构：

[1] Johns Hopkins Univ, Ctr Language & Speech Proc, Baltimore, MD 21218 USA

[2] Google Inc, Mountain View, CA USA

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2014年

关键词：

Deep Neural Network; Keyword Spotting; Embedded Speech Recognition;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Our application requires a keyword spotting system with a small memory footprint, low computational cost, and high precision. To meet these requirements, we propose a simple approach based on deep neural networks. A deep neural network is trained to directly predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score. Keyword recognition results achieve 45% relative improvement with respect to a competitive Hidden Markov Model-based system, while performance in the presence of babble noise shows 39% relative improvement.

引用

页数：5

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