This paper proposes a method for keyword spotting, which first converts utterances to grayscale images via a modified Dynamic Time Warping (DTW) algorithm, and then splits the images into frames which are fed in sequence to a Convolutional Recurrent Deep Neural Network (CRDNN). DTW is employed because of its capability to accurately capture similarities between time sequences, while the neural network exploits the textural features of the DTW matrix for classification. We explore three alternative formulations of the DTW algorithm for extracting the similarity matrices, as well as three different conversion methods from the similarity matrix to a gray-scale image. As opposed to previous works, we employ a recurrent network to consider sequential information encoded in image segments. We perform several evaluations on the TIMIT corpus and find that the system reaches a detection performance of 95%.
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Keyword Spotting using Dynamic Time Warping and Convolutional Recurrent Networks
Semantic Scholar · Computer Science · 2019
Abstract
This paper proposes a method for keyword spotting, which first converts utterances to grayscale images via a modified Dynamic Time Warping (DTW) algorithm, and then splits the images into frames which are fed in sequence to a Convolutional Recurrent Deep Neural Network (CRDNN). DTW is employed because of its capability to accurately capture similarities between time sequences, while the neural network exploits the textural features of the DTW matrix for classification. We explore three alternative formulations of the DTW algorithm for extracting the similarity matrices, as well as three different conversion methods from the similarity matrix to a gray-scale image. As opposed to previous works, we employ a recurrent network to consider sequential information encoded in image segments. We perform several evaluations on the TIMIT corpus and find that the system reaches a detection performance of 95%.