A multi-phase approach for fast spotting of large vocabulary Chinese keywords from Mandarin speech using prosodic information

This paper presents a multi-phase approach for fast spotting of large vocabulary Chinese keywords from a spontaneous Mandarin speech utterance using prosodic knowledge. Without searching through the whole utterance using large number of keyword models, the multi-phase framework proposed including some special scoring schemes provides very good efficiency by considering the monosyllable-based structure of Mandarin Chinese. This approach is therefore very fast due to very good boundary estimations and the deletion of most impossible syllable and keyword candidates using context independent models, and is also very accurate due to the carefully designed scoring processes. A task with 2611 keywords was tested. An inclusion rate of 85.79% for the top 10 candidates is attained, at a speed requiring only 1.2 times that of the utterance length on a Sparc 20 workstation.