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arXiv · 2111.03971

Towards noise robust trigger-word detection with contrastive learning pre-task for fast on-boarding of new trigger-words

Sivakumar Balasubramanian·Aditya Jajodia·Gowtham Srinivasan
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Abstract

Trigger-word detection plays an important role as the entry point of user's communication with voice assistants. But supporting a particular word as a trigger-word involves huge amount of data collection, augmentation and labelling for that word. This makes supporting new trigger-words a tedious and time consuming process. To combat this, we explore the use of contrastive learning as a pre-training task that helps the detection model to generalize to different words and noise conditions. We explore supervised contrastive techniques and also propose a novel self-supervised training technique using chunked words from long sentence audios. We show that both supervised and the new self-supervised contrastive pre-training techniques have comparable results to a traditional classification pre-training on new trigger words with less data availability.

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Sivakumar Balasubramanian, Aditya Jajodia, Gowtham Srinivasan. 2021-11-06. Towards noise robust trigger-word detection with contrastive learning pre-task for fast on-boarding of new trigger-words. https://arxiv.org/abs/2111.03971

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