Discriminative Keyword Spotting
Joseph Keshet, David Grangier and Samy Bengio
In: International Workshop on Non Linear Speech Processing (NOLISP), 22-25 May 2007, Paris, France.
This paper proposes a new approach for keyword spotting, which is not based on HMMs. The proposed method employs a new 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 non-linearly mapping the input acoustic representation of the speech utterance along with the target keyword into an abstract vector space. Building on techniques used for large margin methods for predicting whole sequences, our keyword spotter distills to a classifier in the abstract vector-space which separates speech utterances in which the keyword was uttered from speech utterances in which the keyword was not uttered. We describe a simple iterative algorithm for learning the keyword spotter and discuss its formal properties. Experiments with the TIMIT corpus show that our method outperforms the conventional HMM-based approach.