Learning To Detect Keyword Parts And Whole By Smoothed Max Pooling

• URL: http://arxiv.org/abs/2001.09246v1
• Date: Sat, 25 Jan 2020 01:19:19 GMT
• Title: Learning To Detect Keyword Parts And Whole By Smoothed Max Pooling
• Authors: Hyun-Jin Park, Patrick Violette, Niranjan Subrahmanya
• Abstract summary: We propose smoothed max pooling loss and its application to keyword spotting systems. The proposed approach jointly trains an encoder (to detect keyword parts) and a decoder (to detect whole keyword) in a semi-supervised manner. The proposed new loss function allows training a model to detect parts and whole of a keyword, without strictly depending on frame-level labeling from LVCSR.
• Score: 9.927306160740974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose smoothed max pooling loss and its application to keyword spotting systems. The proposed approach jointly trains an encoder (to detect keyword parts) and a decoder (to detect whole keyword) in a semi-supervised manner. The proposed new loss function allows training a model to detect parts and whole of a keyword, without strictly depending on frame-level labeling from LVCSR (Large vocabulary continuous speech recognition), making further optimization possible. The proposed system outperforms the baseline keyword spotting model in [1] due to increased optimizability. Further, it can be more easily adapted for on-device learning applications due to reduced dependency on LVCSR.

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