| Paper: | SLP-P5.10 | ||
| 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: | UNSUPERVISED CLASS-BASED FEATURE COMPENSATION FOR TIME-VARIABLE BANDWIDTH-LIMITED SPEECH | ||
| Authors: | Nicolas Morales, Universidad Autonoma de Madrid, Spain; Doroteo T. Toledano, ATVS-Universidad Autonoma de Madrid, Spain; John H. L. Hansen, University of Texas, Dallas, United States; Javier Garrido, Jose Colas, Universidad Autonoma de Madrid, Spain | ||
| Abstract: | This paper deals with the problem of speech recognition on band-limited speech. In our previous work we showed how a simple polynomial correction framework could be used for compensation of band-limited speech to minimize the mismatch using full-bandwidth acoustic models. This paper extends this approach to time-varying multiple-channel environments. The compensation framework is extended to perform automatic channel classification prior to compensation, thus allowing for unsupervised multi-channel compensation without the need for an explicit channel classifier. Performance is demonstrated on a wide range of channel bandwidth conditions. This extension makes our compensation approach potentially applicable in a much wider range of scenarios with only very limited performance degradation compared to the supervised approach. | ||
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