Spectral subtraction in Model Distance Maximizing framework for robust speech recognition

This paper has presented a novel discriminative parameters calibration approach based on the model distance maximizing (MDM) to improve the performance of our previous proposed robustness method named spectral subtraction (SS) in likelihood-maximizing framework. In the previous work, for adjusting the spectral over-subtraction factor of SS, conventional ML approach is used that only utilizes the true model without considering other confused models. This makes it very probably to reach a suboptimal solution. While in MDM, by maximizing the dissimilarities among models, the performance of our speech recognizer-based spectral subtraction method could be further improved. Experimental results based on FarsDat database have demonstrated that MDM approach outperformed ML in term of recognition accuracy.