Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model

• URL: http://arxiv.org/abs/2402.05819v1
• Date: Thu, 8 Feb 2024 16:55:21 GMT
• Title: Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model
• Authors: Hung-Chieh Fang, Nai-Xuan Ye, Yi-Jen Shih, Puyuan Peng, Hsuan-Fu Wang, Layne Berry, Hung-yi Lee, David Harwath
• Abstract summary: We propose Pseudo-Word HuBERT (PW-HuBERT), a framework that integrates pseudo word-level targets into the training process. Our experimental results on four spoken language understanding (SLU) benchmarks suggest the superiority of our model in capturing semantic information.
• Score: 57.78191634042409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in self-supervised speech models have shown significant improvement in many downstream tasks. However, these models predominantly centered on frame-level training objectives, which can fall short in spoken language understanding tasks that require semantic comprehension. Existing works often rely on additional speech-text data as intermediate targets, which is costly in the real-world setting. To address this challenge, we propose Pseudo-Word HuBERT (PW-HuBERT), a framework that integrates pseudo word-level targets into the training process, where the targets are derived from a visually-ground speech model, notably eliminating the need for speech-text paired data. Our experimental results on four spoken language understanding (SLU) benchmarks suggest the superiority of our model in capturing semantic information.

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