A novel discriminative score calibration method for keyword search

被引：4

作者：

Lv, Zhiqiang ^{[1
]}

Cai, Meng ^{[1
]}

Zhang, Wei-Qiang ^{[1
]}

Liu, Jia ^{[1
]}

机构：

[1] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China

来源：

17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5: UNDERSTANDING SPEECH PROCESSING IN HUMANS AND MACHINES | 2016年

基金：

中国国家自然科学基金;

关键词：

keyword search; score calibration; CTC; neural network;

D O I：

10.21437/Interspeech.2016-606

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

The performance of keyword search systems depends heavily on the quality of confidence scores. In this work, a novel discriminative score calibration method has been proposed. By training an MLP classifier employing the word posterior probability and several novel normalized scores, we can obtain a relative improvement of 4.67% for the actual term-weighted value (ATWV) metric on the OpenKWS15 development test dataset. In addition, a LSTM-CTC based keyword verification method has been proposed to supply extra acoustic information. After the information is added, a further improvement of 7.05% over the baseline can be observed.

引用

页码：745 / 749

页数：5

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