Adaptation techniques for ambience and microphone compensation in the IBM Tangora speech recognition system

Conventional speech recognition systems such as the IBM Tangora tend to be adversely influenced by external factors such as background noise and microphone characteristics. This paper discusses some adaptation techniques to counteract such influences. We also consider ways to reduce computation so that the methods may be implemented for real time Tangora operation. The adaptation strategy utilized two sets of VQ codebooks derived from the training data, one representing the ambience and the other characterizing the speech domain. A speech versus ambience decision was made by examining several factors, such as a comparison of the overall energy in a frame with a percentile point of a running energy histogram. We include results to demonstrate the effectiveness of our approach