Fast decoding in large vocabulary name dialing

The fast decoding problem is a key challenge virtually in all practical real-time speech recognition systems since model decoding is still by far the most time-consuming operation in Automatic Speech Recognition (ASR) systems. In current speech recognizers, there is typically a trade-off between the desired vocabulary size, the processing power available for speech recognition, and the recognition accuracy. Fast decoding methods are often needed in order to meet the real-time requirements set for a system. The use of these methods may of course not degrade the recognition accuracy. In this paper, we investigate the performance of efficient decoding methods in large vocabulary name dialing. Tree-structured lexicon, fast observation probability evaluation, and adaptive Viterbi beam search are developed and integrated in a name dialing system. The system is tested with lexicons ranging from 100 to 3000 entries. With the lexicon of 1000 words the utilization of the fast decoding methods speeds up the system by 282%. The speed-up degrades the recognition accuracy as little as 0.95%.