Discriminative Keyword Spotting
Joseph Keshet, David Grangier and Samy Bengio
This paper proposes a new approach for keyword spotting, which is not based on HMMs. Unlike previous approaches, the proposed method employs a discriminative learning procedure, in which the learning phase aims at maximizing the area under the ROC curve, as this quantity is the most common measure to evaluate keyword spotters. The keyword spotter we devise is based on mapping the input acoustic representation of the speech utterance along with the target keyword into a vector space. Building on techniques used for large margin and kernel methods for predicting whole sequences, our keyword spotter distills to a classifier in this vector-space, which separates speech utterances in which the keyword is uttered from speech utterances in which the keyword is not uttered. We describe a simple iterative algorithm for training a keyword spotter and discuss its formal properties. Experiments with the TIMIT corpus show that our method outperforms the conventional HMM-based approach. Further experiments using the TIMIT trained model, but tested on the WSJ dataset, show that without further training our method outperforms the conventional HMM-based approach.