Spotting subsequences matching an HMM using the average observation probability criteria with application to keyword spotting

Abstract

This paper addresses the problem of detecting keywords in unconstrained speech. The proposed algorithms search for the speech segment maximizing the average observation probability 1 along the most likely path in the hypothesized keyword model. As known, this approach (sometimes referred to as sliding model method) requires a relaxation of the begin/endpoints of the Viterbi matching, as well as a time normalization of the resulting score. This makes solutions 2 L