Text independent speaker recognition using speaker dependent word spotting

This paper is motivated by the fact that text dependent speaker recognition is inherently more accurate than text independent speaker recognition. In this work we assign models to frequent words spoken by a speaker and spot them in a test call. In this way, text-dependent speaker recognition technology can be used for text independent tasks. The approach we take is to use DTW (Dynamic Time Warp) word spotting to find words in the test that resemble words in the train set. Results on the SPIDRE corpus show that using a combined DTW spotter based system and a GMM system improves performance significantly. For very low false acceptance rate (0.1%) misdetection was reduced from 32.2% to 23.3% (28% reduction). For low false acceptance rate (1%) misdetection was reduced from 28.9% to 21.1% (27% reduction).