Fast Speech Recognition for Voice Destination Entry in a Car Navigation System

In this paper, we introduce a multi-stage decoding algorithm optimized to recognize very large number of entry names on a resource-limited embedded device. The multi-stage decoding algorithm is composed of a two-stage HMM-based coarse search and a detailed search. The two-stage HMM-based coarse search generates a small set of candidates that are assumed to contain a correct hypothesis with high probability, and the detailed search re-ranks the candidates by rescoring them with sophisticate acoustic models. In this paper, we take experiments with I-millions of point-of-interest (POI) names on an in-car navigation device with a fixed-point processor running at 620MHz. The experimental result shows that the multi-stage decoding algorithm runs about 2.23 times real-time on the device without serious degradation of recognition performance.