Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting

Beltrán Labrador; Guanlong Zhao; Ignacio López Moreno; Angelo Scorza Scarpati; Liam Fowl; Quan Wang

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arXiv · 2211.06478

Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting

Beltrán Labrador·Guanlong Zhao·Ignacio López Moreno·Angelo Scorza Scarpati·Liam Fowl·Quan Wang

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Abstract

In this paper, we present a novel approach to adapt a sequence-to-sequence Transformer-Transducer ASR system to the keyword spotting (KWS) task. We achieve this by replacing the keyword in the text transcription with a special token and training the system to detect the token in an audio stream. At inference time, we create a decision function inspired by conventional KWS approaches, to make our approach more suitable for the KWS task. Furthermore, we introduce a specific keyword spotting loss by adapting the sequence-discriminative Minimum Bayes-Risk training technique. We find that our approach significantly outperforms ASR based KWS systems. When compared with a conventional keyword spotting system, our proposal has similar performance while bringing the advantages and flexibility of sequence-to-sequence training. Additionally, when combined with the conventional KWS system, our approach can improve the performance at any operation point.

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Beltrán Labrador, Guanlong Zhao, Ignacio López Moreno, Angelo Scorza Scarpati, Liam Fowl, Quan Wang. 2022-11-11. Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting. https://arxiv.org/abs/2211.06478

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