An HMM Compensation Approach for Dynamic Features Using Unscented Transformation and its Application to Noisy Speech Recognition

In our previous work, a new HMM compensation approach for static MFCC features was proposed by using a technique called Unscented Transformation (UT). Three implementations of the UT approach with different computational complexities were evaluated on Aurora2 connected digits database, and significant performance improvements were achieved compared to log-normal- approximation-based PMC (Parallel Model Combination) and first- order-approximation-based VTS (Vector Taylor Series) approaches. In this paper, we extend our UT-based formulation to compensating for HMM parameters corresponding to both static and dynamic features. New experimental results on Aurora2 task are reported to demonstrate the effectiveness of the proposed UT approach.