Integrating Self-Supervised Speech Model with Pseudo Word-Level Targets from Visually-Grounded Speech Model
Part Of
2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings
Journal Volume
33
Start Page
645
End Page
649
ISBN (of the container)
979-835037451-3
Date Issued
2024-04-14
Author(s)
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.
Event(s)
49th IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024
Publisher
IEEE
Type
conference paper