Keyword Spotting Based on Syllable Confusion Network

Keyword spotting becomes a very important branch of speech recognition. But the acoustic mismatch between training and testing environments often causes a severe degradation in the recognition performance. This paper presents an improved keyword spotting strategy. A fuzzy search algorithm is proposed to extract keyword hypotheses from a syllable confusion network (SCN). SCN is linear and naturally suitable for indexing. To accelerate search process, SCN are pruned to feasible sizes. As a post-processing method, minimum classification error (MCE) optimized confidence measure is adopted to reject false accepts. On Mandarin conversational telephone speech (CTS), the proposed algorithms reduce the equal error rate (EER) by 7.2% relative.