Abstract
The performance of current automatic speech recognition (ASR) systems often degrades dramatically when the input speech is corrupted by various kinds of noise sources. In this chapter, we first discuss several prominently-used and effective distribution-based feature compensation methods to improving ASR robustness, and then review two polynomial regression methods that have the merit of directly characterizing the relationship between speech features and their corresponding distribution characteristics to compensate for noise interference. All these methods were thoroughly investigated and compared using the Aurora-2 standard database and task. The empirical results demonstrate that most of these distribution-based feature compensation methods can achieve considerable word error rate reductions over the baseline system for either clean-condition or multi-condition training settings.
Keywords: Distribution-based compensation, ASR robustness, polynomial regression methods
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