SpeechCLIP: Integrating Speech with Pre-Trained Vision and Language Model

• URL: http://arxiv.org/abs/2210.00705v1
• Date: Mon, 3 Oct 2022 04:15:36 GMT
• Title: SpeechCLIP: Integrating Speech with Pre-Trained Vision and Language Model
• Authors: Yi-Jen Shih, Hsuan-Fu Wang, Heng-Jui Chang, Layne Berry, Hung-yi Lee, David Harwath
• Abstract summary: SpeechCLIP is a novel framework bridging speech and text through images to enhance speech models without transcriptions. We leverage state-of-the-art pre-trained HuBERT and CLIP, aligning them via paired images and spoken captions with minimal fine-tuning.
• Score: 56.49878599920353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven speech processing models usually perform well with a large amount of text supervision, but collecting transcribed speech data is costly. Therefore, we propose SpeechCLIP, a novel framework bridging speech and text through images to enhance speech models without transcriptions. We leverage state-of-the-art pre-trained HuBERT and CLIP, aligning them via paired images and spoken captions with minimal fine-tuning. SpeechCLIP outperforms prior state-of-the-art on image-speech retrieval and performs zero-shot speech-text retrieval without direct supervision from transcriptions. Moreover, SpeechCLIP can directly retrieve semantically related keywords from speech.

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