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.
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Zhou, X., Zhuang, X., Liu, M., Tang, H., Hasegawa-Johnson, M., Huang, T. (2008). HMM-Based Acoustic Event Detection with AdaBoost Feature Selection. In: Stiefelhagen, R., Bowers, R., Fiscus, J. (eds) Multimodal Technologies for Perception of Humans. RT CLEAR 2007 2007. Lecture Notes in Computer Science, vol 4625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68585-2_33
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DOI: https://doi.org/10.1007/978-3-540-68585-2_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68584-5
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