Performance evaluation for an HMM-based keyword spotter and a large-margin based one in noisy environments

AbstractKeyword spotting refers to the detection of a limited number of given keywords in speech utterances. In this paper, first we review one of the large margin based keyword spotting approach that uses a discriminative method for training the keyword spotter. Then, we evaluate the robustness of this approach in different noisy conditions. In addition; we compare the performance of this method with an HMM-based keyword spotter -which uses a generative training method- in the same noisy conditions. The experimental results show that the large-margin based keyword spotter is more robust than HMM-based system in noisy environments