An HMM Compensation Approach Using Unscented Transformation for Noisy Speech Recognition

The performance of current HMM-based automatic speech recognition (ASR) systems degrade significantly in real-world applications where there exist mismatches between training and testing conditions caused by factors such as mismatched signal capturing and transmission channels and additive environmental noises. Among many approaches proposed previously to cope with the above robust ASR problem, two notable HMM compensation approaches are the so-called Parallel Model Combination (PMC) and Vector Taylor Series (VTS) approaches, respectively. In this paper, we introduce a new HMM compensation approach using a technique called Unscented Transformation (UT). As a first step, we have studied three implementations of the UT approach with different computational complexities for noisy speech recognition, and evaluated their performance on Aurora2 connected digits database. The UT approaches achieve significant improvements in recognition accuracy compared to log-normal-approximation-based PMC and first-order-approximation-based VTS approaches.