Adaptive transition bias for robust low complexity speech recognition

The basis for all methods described in this paper is the application of an adaptive transition bias to the sequences of phoneme models that represent spoken utterances. This offers significantly improved accuracy in phoneme based speaker independent recognition, while adding very little overhead to the overall system complexity. The algorithms are tested using the low complexity hybrid recognizer denoted hidden neural networks (HNN) on US English and Japanese speaker independent name dialing tasks. Experimental results show that our approach provides a relative error rate reduction of up to 47% over the baseline system.