MATS: An Audio Language Model under Text-only Supervision

Wen Wang, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:63016-63035, 2025.

Abstract

Large audio-language models (LALMs), built upon powerful Large Language Models (LLMs), have exhibited remarkable audio comprehension and reasoning capabilities. However, the training of LALMs demands a large corpus of audio-language pairs, which requires substantial costs in both data collection and training resources. In this paper, we propose MATS, an audio-language multimodal LLM designed to handle Multiple Audio task using solely Text-only Supervision. By leveraging pre-trained audio-language alignment models such as CLAP, we develop a text-only training strategy that projects the shared audio-language latent space into LLM latent space, endowing the LLM with audio comprehension capabilities without relying on audio data during training. To further bridge the modality gap between audio and language embeddings within CLAP, we propose the Strongly-related noisy text with audio (Santa) mechanism. Santa maps audio embeddings into CLAP language embedding space while preserving essential information from the audio input. Extensive experiments demonstrate that MATS, despite being trained exclusively on text data, achieves competitive performance compared to recent LALMs trained on large-scale audio-language pairs. The code is publicly available in https://github.com/wangwen-banban/MATS

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-wang25ai, title = {{MATS}: An Audio Language Model under Text-only Supervision}, author = {Wang, Wen and Hou, Ruibing and Chang, Hong and Shan, Shiguang and Chen, Xilin}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {63016--63035}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/wang25ai/wang25ai.pdf}, url = {https://proceedings.mlr.press/v267/wang25ai.html}, abstract = {Large audio-language models (LALMs), built upon powerful Large Language Models (LLMs), have exhibited remarkable audio comprehension and reasoning capabilities. However, the training of LALMs demands a large corpus of audio-language pairs, which requires substantial costs in both data collection and training resources. In this paper, we propose MATS, an audio-language multimodal LLM designed to handle Multiple Audio task using solely Text-only Supervision. By leveraging pre-trained audio-language alignment models such as CLAP, we develop a text-only training strategy that projects the shared audio-language latent space into LLM latent space, endowing the LLM with audio comprehension capabilities without relying on audio data during training. To further bridge the modality gap between audio and language embeddings within CLAP, we propose the Strongly-related noisy text with audio (Santa) mechanism. Santa maps audio embeddings into CLAP language embedding space while preserving essential information from the audio input. Extensive experiments demonstrate that MATS, despite being trained exclusively on text data, achieves competitive performance compared to recent LALMs trained on large-scale audio-language pairs. The code is publicly available in https://github.com/wangwen-banban/MATS} }
Endnote
%0 Conference Paper %T MATS: An Audio Language Model under Text-only Supervision %A Wen Wang %A Ruibing Hou %A Hong Chang %A Shiguang Shan %A Xilin Chen %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-wang25ai %I PMLR %P 63016--63035 %U https://proceedings.mlr.press/v267/wang25ai.html %V 267 %X Large audio-language models (LALMs), built upon powerful Large Language Models (LLMs), have exhibited remarkable audio comprehension and reasoning capabilities. However, the training of LALMs demands a large corpus of audio-language pairs, which requires substantial costs in both data collection and training resources. In this paper, we propose MATS, an audio-language multimodal LLM designed to handle Multiple Audio task using solely Text-only Supervision. By leveraging pre-trained audio-language alignment models such as CLAP, we develop a text-only training strategy that projects the shared audio-language latent space into LLM latent space, endowing the LLM with audio comprehension capabilities without relying on audio data during training. To further bridge the modality gap between audio and language embeddings within CLAP, we propose the Strongly-related noisy text with audio (Santa) mechanism. Santa maps audio embeddings into CLAP language embedding space while preserving essential information from the audio input. Extensive experiments demonstrate that MATS, despite being trained exclusively on text data, achieves competitive performance compared to recent LALMs trained on large-scale audio-language pairs. The code is publicly available in https://github.com/wangwen-banban/MATS
APA
Wang, W., Hou, R., Chang, H., Shan, S. & Chen, X.. (2025). MATS: An Audio Language Model under Text-only Supervision. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:63016-63035 Available from https://proceedings.mlr.press/v267/wang25ai.html.

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