| Paper: | SLP-P5.1 | ||
| Session: | Feature-based Robust Speech Recognition | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Feature-based Robust Speech Recognition (e.g., noise, etc) | ||
| Title: | Speech Feature Estimation Under The Presence Of Noise With A Switching Linear Dynamic Model | ||
| Authors: | Jianping Deng, Martin Bouchard, Tet Hin Yeap, University of Ottawa, Canada | ||
| Abstract: | This paper presents an approach to enhance speech feature estimation in the log spectral domain under noisy environments. A higher-order switching linear dynamic model (SLDM) is explored as a parametric model for the clean speech distribution, which enforces a state transition in the feature space and captures the smooth time evolution of speech conditioned on the state sequence. The clean speech components are estimated by means of an Interacting Multiple Model (IMM) algorithm. Experimental results show that increasing the order of linear dynamic model in SLDM and the introduction of transition probabilities among linear dynamic models can further improve the performance of SLDM systems in feature compensation for robust speech recognition. | ||
©2018 Conference Management Services, Inc. -||- email: webmaster@icassp2006.org -||- Last updated Friday, August 17, 2012