Development of a Chinese song name recognition system

The development of automatic speech recognition (ASR) technology in recent years has made it possible for some intelligent query systems to use a voice interface. Automatic song selection is a practical and interesting application of ASR. In this paper we describe our efforts to build and improve a Chinese song name recognition system. It is a large vocabulary, speaker-independent system currently in commercial use. This is a typical example of large list recognition tasks. We use a new paradigm for lager list recognition. In this framework, the spoken query is recognized by a LVCSR module firstly and then the recognized result is used to search for the final song name. Unlike transcription tasks, such as Switchboard task, our LVCSR module is an in-domain application. We present some innovative optimizations that improve the song name recognition accuracy. The experimental result shows that there techniques make relative 7.36%, 26.87% and 32.71% error rate reduction for top 1, top 5 and top 10 results respectively upon the conventional grammar-constrained recognizer.