Unsupervised Class-Based Feature Compensation for Time-Variable Bandwidth-Limited Speech

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