Keyword spotting in auto-attendant system

In this paper, an auto-attendant system using finite state grammar (FSG) based on a continuous speech recognition (CSR) model is introduced. However, by using two virtual garbage models, one is to match the leading extraneous speech before the key name and the other to match the tailing extraneous speech following the key name, we managed to reach a more flexible and robust auto-attendant system. The experiment result show that, in our auto attendant system (about 240 names), to the name only test set and the sentence test set 1 composed of sentences that FSG can recognize, the recognition rate of the keyword spotting system is almost the same as that of FSG. To the sentence test set 2 composed of sentences that undefined in the FSG the keyword spotting system outperforms the FSG system remarkably. Not affecting the recognition accuracy of name only test set and the sentence test set 1, task dependent keyword models cut off additional 20% of error rate comparing with task independent keyword models in the sentence test set 2.