| 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. | ||
| Back | |||
Home -||-
Organizing Committee -||-
Technical Committee -||-
Technical Program -||-
Plenaries
Paper Submission -||-
Special Sessions -||-
ITT -||-
Paper Review -||-
Exhibits -||-
Tutorials
Information -||-
Registration -||-
Travel Insurance -||-
Housing -||-
Workshops