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| Paper: | SP-P15.3 |
| Session: | Robustness in Noisy Environments |
| Time: | Friday, May 21, 15:30 - 17:30 |
| Presentation: |
Poster |
| Topic: |
Speech Processing: Robust Speech Recognition |
| Title: |
JOINT REMOVAL OF ADDITIVE AND CONVOLUTIONAL NOISE WITH MODEL-BASED FEATURE ENHANCEMENT |
| Authors: |
Veronique Stouten; Katholieke Universiteit Leuven | | |
| | Hugo Van hamme; Katholieke Universiteit Leuven | | |
| | Patrick Wambacq; Katholieke Universiteit Leuven | | |
| Abstract: |
In this paper we describe how we successfully extended the Model-Based Feature Enhancement (MBFE)-algorithm to jointly remove additive and convolutional noise from corrupted speech. Although a model of the clean speech can incorporate prior knowledge into the feature enhancement process, this model no longer yields an accurate fit if a different microphone is used. To cure the resulting performance degradation, we merge a new iterative EM-algorithm to estimate the channel, and the MBFE-algorithm to remove non-stationary additive noise. In the latter, the parameters of a shifted clean speech HMM and a noise HMM are first combined by a Vector Taylor Series approximation and then the state-conditional MMSE-estimates of the clean speech are calculated. Recognition experiments confirmed the superior performance on the Aurora4 recognition task. An average relative reduction in WER of 12% and 2.8% on the clean and multi condition training respectively, was obtained compared to the Advanced Front-End standard. |
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