A Bayesian Approach to Estimation of Speaker Normalization Parameters
Table 4: Table illustrating the Speech Recognition Performance using normalization in terms of Word Error Rate (WER) and its improvement over the baseline case
['[BOLD] Normalization Methods', '[BOLD] Commodity Database  [BOLD] Word Error Rate (%)', '[BOLD] Commodity Database  [BOLD] Improvement(%)', '[BOLD] District Database  [BOLD] Word Error Rate (%)', '[BOLD] District Database  [BOLD] Improvement(%)']
The recognition performance for different experiments is evaluated using Word Error Rate (WER). First row of this table corresponds to the baseline case, when there is no speaker normalization being used. All the following rows display performance using affine model. Performance using normalization is compared with baseline case and relative improvements are indicated in a separate column. A negative entry in this column signifies performance degradation. Note that, background noise can significantly degrade the recognition performance. In the standard Aurora-2 task for recognising the ten digits and ‘oh’ in American English (i.e. only 11 word vocabulary), they have shown that the performance can vary from 99% to 10% depending on the background noise. In this work also, the speech data in both databases contain large amount of background noise which justifies high WER. The results using normalization indicate that, changing the normalization model from linear to affine, performance can be improved. Among all estimation techniques used, only Bayesian estimation method improves performance of recognizer. This improvement is almost twice than that of using linear model.