A new keyword spotting approach based on iterative dynamic programming

This paper addresses the problem of detecting keywords in unconstrained speech without explicit modeling of non-keyword segments. The proposed algorithm is based on recent developments in confidence measures using local posterior probabilities, and searches for the segment maximizing the average observation posteriori 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, making dynamic programming sub-optimal or more complex (more computation and/or more memory). We present here an alternative (quite simple and efficient) solution to this problem, using an iterative form of Viterbi decoding algorithm, but which does not require scoring for all possible begin/endpoints. Convergence proof of this algorithm is available (Silaghi and Bourlard, 1999). Results obtained with this method on 100 keywords chosen at random from the BREF database are reported.