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arXiv · 2303.04255

Self-supervised speech representation learning for keyword-spotting with light-weight transformers

Chenyang Gao·Yue Gu·Francesco Caliva·Yuzong Liu
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Abstract

Self-supervised speech representation learning (S3RL) is revolutionizing the way we leverage the ever-growing availability of data. While S3RL related studies typically use large models, we employ light-weight networks to comply with tight memory of compute-constrained devices. We demonstrate the effectiveness of S3RL on a keyword-spotting (KS) problem by using transformers with 330k parameters and propose a mechanism to enhance utterance-wise distinction, which proves crucial for improving performance on classification tasks. On the Google speech commands v2 dataset, the proposed method applied to the Auto-Regressive Predictive Coding S3RL led to a 1.2% accuracy improvement compared to training from scratch. On an in-house KS dataset with four different keywords, it provided 6% to 23.7% relative false accept improvement at fixed false reject rate. We argue this demonstrates the applicability of S3RL approaches to light-weight models for KS and confirms S3RL is a powerful alternative to traditional supervised learning for resource-constrained applications.

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Chenyang Gao, Yue Gu, Francesco Caliva, Yuzong Liu. 2023-03-07. Self-supervised speech representation learning for keyword-spotting with light-weight transformers. https://arxiv.org/abs/2303.04255

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