HMM-based acoustic event detection with AdaBoost feature selection

被引：0

作者：

Zhou, Xi ^{[1
]}

Zhuang, Xiaodan ^{[1
]}

Liu, Ming ^{[1
]}

Tang, Hao ^{[1
]}

Hasegawa-Johnson, Mark ^{[1
]}

Huang, Thomas ^{[1
]}

机构：

[1] Univ Illinois, Beckman Inst, Dept Elect & Comp Engn, Urbana, IL 61801 USA

来源：

MULTIMODAL TECHNOLOGIES FOR PERCEPTION OF HUMANS | 2008年 / 4625卷

基金：

美国国家科学基金会;

关键词：

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：

Because of the spectral difference between speech and acoustic events, we propose using Kullback-Leibler distance to quantify the discriminant capability of all speech feature components in acoustic event detection. Based on these distances, we use AdaBoost to select a discriminant feature set and demonstrate that this feature set outperforms classical speech feature set such as MFCC in one-pass HMM-based acoustic event detection. We implement an HMM-based acoustic events detection system with lattice rescoring using a feature set selected by the above AdaBoost based approach.

引用

页码：345 / 353

页数：9

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