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.
Original language | English (US) |
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Pages (from-to) | 345-353 |
Number of pages | 9 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume | 4625 LNCS |
DOIs | |
State | Published - 2008 |
Event | 2nd Annual Classifcation of Events Activities and Relationships, CLEAR 2007 and Rich Transcription, RT 2007 - Baltimore, MD, United States Duration: May 8 2007 → May 11 2007 |
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science