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arXiv · 2501.03523

Vocal Tract Length Warped Features for Spoken Keyword Spotting

Achintya kr. Sarkar·Priyanka Dwivedi·Zheng-Hua Tan
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Abstract

In this paper, we propose several methods that incorporate vocal tract length (VTL) warped features for spoken keyword spotting (KWS). The first method, VTL-independent KWS, involves training a single deep neural network (DNN) that utilizes VTL features with various warping factors. During training, a specific VTL feature is randomly selected per epoch, allowing the exploration of VTL variations. During testing, the VTL features with different warping factors of a test utterance are scored against the DNN and combined with equal weight. In the second method scores the conventional features of a test utterance (without VTL warping) against the DNN. The third method, VTL-concatenation KWS, concatenates VTL warped features to form high-dimensional features for KWS. Evaluations carried out on the English Google Command dataset demonstrate that the proposed methods improve the accuracy of KWS.

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BibTeXRIS

Achintya kr. Sarkar, Priyanka Dwivedi, Zheng-Hua Tan. 2025-01-07. Vocal Tract Length Warped Features for Spoken Keyword Spotting. https://arxiv.org/abs/2501.03523

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