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

• URL: http://arxiv.org/abs/2111.03971v1
• Date: Sat, 6 Nov 2021 22:39:05 GMT
• Title: Towards noise robust trigger-word detection with contrastive learning pre-task for fast on-boarding of new trigger-words
• Authors: Sivakumar Balasubramanian, Aditya Jajodia, Gowtham Srinivasan
• Abstract summary: Trigger-word detection plays an important role as the entry point of user's communication with voice assistants. We propose contrastive learning as a pre-training task that helps the detection model to generalize to different words and noise conditions. We show that the contrastive pre-training techniques have comparable results to a traditional classification pre-training on new trigger words with less data availability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 self-supervised technique using chunked words from long sentence audios. We show that the 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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