Speech adaptation using neural networks for connected digit recognition
Xuelin Cheng, Han Wang, Zongge Li · 2002
The performance of speech recognizers is usually degraded when used in different environments due to varied channels, speech rate and so on. Retraining the recognizers demands a large amount of new data recorded under new environments. On the contrary adaptation can fit the characteristics of the new environments by using only a small amount of data. In this paper a neural network based adaptation was applied to enhance the performance of connected digit recognition system because of its ability of computing the nonlinear function. A baseline system was built on OGI Number corpus, which had 97.76% word accuracy and 85.19% sentence accuracy on itself. However when tested on the Australia English Telephone Speech Database the performance greatly decreased to 71.86% and 16.67% respectively. To avoid retraining the recognizer a feed-forward backpropagation network was used to fit the characteristics of new data, and reduced the error rate by 53% when combined with maximum likelihood linear regression.