Spoken Term Detection Using Dynamic Match Subword Confusion Network

This paper details our subword confusion network based approach for Mandarin spoken term detection. As well as the system description, two approaches are presented for improvement of our baseline system. To reduce the inherent high recognition error of the subword decoding system due to its weak language model constraints, the subword confusion network is proposed to be generated from the word decoding system. In addition, a variant of minimum edit distance method (MED) is proposed for linearly scanning the confusion networks for spoken term detection, which incorporates the confidence from confusion networks and other sources. A real-time term detector is constructed based on the modified MED method. Experiments show significant performance improvement from the word decoding and slight improvement from the real-time detector compared to our baseline system.