A new keyword spotting approach based on reward function

In this paper, we compare the performance achieved by different word-spotting techniques based on hidden Markov models. We propose two methods to detect keywords, the first one uses a GMM (Gaussian mixture model) as a filler model to absorb the out-of-vocabulary words. The second is an alternative approach which does not attempt to model out-of-vocabulary words, instead, it uses a loop phonemes based grammar. Furthermore, it uses different reward functions to favour the recognition of the keywords phonemes.