WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models

• URL: http://arxiv.org/abs/2203.15863v1
• Date: Tue, 29 Mar 2022 19:08:55 GMT
• Title: WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models
• Authors: Heting Gao, Junrui Ni, Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson
• Abstract summary: Large-scale auto-regressive language models pretrained on massive text have demonstrated their impressive ability to perform new natural language tasks. Recent studies further show that such a few-shot learning ability can be extended to the text-image setting by training an encoder to encode the images into embeddings. We propose a novel speech understanding framework, WavPrompt, where we finetune a wav2vec model to generate a sequence of audio embeddings understood by the language model.
• Score: 57.557319372969495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale auto-regressive language models pretrained on massive text have demonstrated their impressive ability to perform new natural language tasks with only a few text examples, without the need for fine-tuning. Recent studies further show that such a few-shot learning ability can be extended to the text-image setting by training an encoder to encode the images into embeddings functioning like the text embeddings of the language model. Interested in exploring the possibility of transferring the few-shot learning ability to the audio-text setting, we propose a novel speech understanding framework, WavPrompt, where we finetune a wav2vec model to generate a sequence of audio embeddings understood by the language model. We show that WavPrompt is a few-shot learner that can perform speech understanding tasks better than a naive text baseline. We conduct detailed ablation studies on different components and hyperparameters to empirically identify the best model configuration. In addition, we conduct a non-speech understanding experiment to show WavPrompt can extract more information than just the transcriptions.

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