Noise adaptation for robust AURORA 2 noisy digit recognition using statistical data mapping
Xuechuan Wang, Douglas D. O’Shaughnessy · 2004
The mismatch between system training and operat-ing conditions often has negative influences on automatic speech recognition (ASR) systems. Noise in the operat-ing environments is commonly encountered. ASR model adaptation is an important way to enhance the system per-formance in noisy environments. This paper proposes a feature-based statistical data mapping (SDM) approach for robust noisy digit recognition. The recognition tasks are carried out on the AURORA 2 database. Compared to other model adaptation methods such as MLLR, the SDM approach has more robust performances. 1.