HMM-Based Acoustic Event Detection with AdaBoost Feature Selection

Given 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.

• Type: Conference
• Year: 2007
• Where: CLEAR
• Authors: Xi Zhou, Xiaodan Zhuang, Ming Liu, Hao Tang, Mark Hasegawa-Johnson, Thomas S. Huang
• Keywords: Acoustic Event Detection, Acoustic Events, Biometrics, CLEAR 2007, Hmm-based Acoustic Events