Speaker dependent activation keyword detector based on GMM-UBM

In this paper, we present a new method for isolated keyword detection that is meant to activate a personal device from standby state. Instead of using the common method for speech recognition such as Hidden Markov Model (HMM) or Dynamic Time Warping (DTW), we modify a GMM-UBM (Gaussian Mixture Model – Universal Background Model) scheme that is better known in speaker recognition field. Since only one adapted Gaussian mixture is used to represent the keyword, a second layer of check is employed to ensure the right sequence of occurrence within the keyword. This is done by comparing it with the Longest Common Subsequence (LCS) of the highest performing GMM component obtained during the registration phase. Results for a subset of the SpeechDat-Car database are presented to validate the benefit of this modeling against moderate noise level.