Improving Voice Trigger Detection with Metric Learning

• URL: http://arxiv.org/abs/2204.02455v1
• Date: Tue, 5 Apr 2022 18:59:27 GMT
• Title: Improving Voice Trigger Detection with Metric Learning
• Authors: Prateeth Nayak, Takuya Higuchi, Anmol Gupta, Shivesh Ranjan, Stephen Shum, Siddharth Sigtia, Erik Marchi, Varun Lakshminarasimhan, Minsik Cho, Saurabh Adya, Chandra Dhir, Ahmed Tewfik
• Abstract summary: We propose a novel voice trigger detector that can use a small number of utterances from a target speaker to improve detection accuracy. A personalized voice trigger score is then obtained as a similarity score between the embeddings of enrollment utterances and a test utterance. Experimental results show that the proposed approach achieves a 38% relative reduction in a false rejection rate.
• Score: 15.531040328839639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Voice trigger detection is an important task, which enables activating a voice assistant when a target user speaks a keyword phrase. A detector is typically trained on speech data independent of speaker information and used for the voice trigger detection task. However, such a speaker independent voice trigger detector typically suffers from performance degradation on speech from underrepresented groups, such as accented speakers. In this work, we propose a novel voice trigger detector that can use a small number of utterances from a target speaker to improve detection accuracy. Our proposed model employs an encoder-decoder architecture. While the encoder performs speaker independent voice trigger detection, similar to the conventional detector, the decoder predicts a personalized embedding for each utterance. A personalized voice trigger score is then obtained as a similarity score between the embeddings of enrollment utterances and a test utterance. The personalized embedding allows adapting to target speaker's speech when computing the voice trigger score, hence improving voice trigger detection accuracy. Experimental results show that the proposed approach achieves a 38% relative reduction in a false rejection rate (FRR) compared to a baseline speaker independent voice trigger model.

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