Blind Feature Compensation for Time-Variant Band-Limited Speech Recognition

Abstract

Mismatch in speech bandwidth between training and real operation greatly affects automatic speech recognition. This letter extends previous work on feature compensation of band-limited speech to establish a framework for blind compensation of speech data of unknown bandwidth, valid even when the distortion (band-limiting channel) changes rapidly and continuously in time. The available bandwidth of the input speech signal is automatically detected, and the band-limited feature vectors are compensated prior to being compared against full-bandwidth acoustic models. For a fixed bandwidth limitation, phoneme recognition performance using the proposed method is similar to that achieved by models adapted to match the distortion. However, compared to model adaptation approaches, this new approach can seamlessly be extended to rapidly time-varying conditions while maintaining low computational and memory costs