HMM-Based Acoustic Event Detection with AdaBoost Feature Selection

Authors:
Xi Zhou;Xiaodan Zhuang;Ming Liu;Hao Tang;Mark Hasegawa-Johnson;Thomas Huang
Affiliations:
Beckman Institute Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, USA IL 61801;Beckman Institute Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, USA IL 61801;Beckman Institute Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, USA IL 61801;Beckman Institute Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, USA IL 61801;Beckman Institute Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, USA IL 61801;Beckman Institute Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign (UIUC), Urbana, USA IL 61801
Venue:
Multimodal Technologies for Perception of Humans
Year:
2008

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Abstract

Because of the spectral difference between speech and acous- tic 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.