research-article

Boosting Speech Recognition Robustness to Modality-Distortion with Contrast-Augmented Prompts

Authors:

Tao JinAuthors Info & Claims

MM '24: Proceedings of the 32nd ACM International Conference on Multimedia

Pages 3838 - 3847

https://doi.org/10.1145/3664647.3681347

Published: 28 October 2024 Publication History

Abstract

In the burgeoning field of Audio-Visual Speech Recognition (AVSR), extant research has predominantly concentrated on the training paradigms tailored for high-quality resources. However, owing to the challenges inherent in real-world data collection, audio-visual data are frequently affected by modality-distortion, which encompasses audio-visual asynchrony, video noise and audio noise. The recognition accuracy of existing AVSR method is significantly compromised when multiple modality-distortion coexist in low-resource data. In light of the above challenges, we propose PCD: cluster-Prompt with Contrastive Decomposition, a robust framework for modality-distortion speech recognition, specifically devised to transpose the pre-trained knowledge from high-resource domain to the targeted domain by leveraging contrast-augmented prompts. In contrast to previous studies, we take into consideration the possibility of various types of distortion in both the audio and visual modalities. Concretely, we design bespoke prompts to delineate each modality-distortion, guiding the model to achieve speech recognition applicable to various distortion scenarios with quite few learnable parameters. To materialize the prompt mechanism, we employ multiple cluster-based strategies that better suits the pre-trained audio-visual model. Additionally, we design a contrastive decomposition mechanism to restrict the explicit relationships among various modality conditions, given their shared task knowledge and disparate modality priors. Extensive results on LRS2 dataset demonstrate that PCD achieves state-of-the-art performance for audio-visual speech recognition under the constraints of distorted resources. Code is available at https://github.com/ballooncatt/PCD.

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Cited By

Jin TYan WWang YCai SShuai QZhao ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Calibrating Prompt from History for Continual Vision-Language Retrieval and GroundingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681387(4302-4311)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681387
Yang XCheng XFu DFang MZuo JJi SZhao ZTao JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)SyncTalklip: Highly Synchronized Lip-Readable Speaker Generation with Multi-Task LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681386(8149-8158)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681386
Yan WWang YLin WGuo ZZhao ZJin TCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Low-rank Prompt Interaction for Continual Vision-Language RetrievalProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681264(8257-8266)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681264

Index Terms

Boosting Speech Recognition Robustness to Modality-Distortion with Contrast-Augmented Prompts
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
    2. Natural language processing
      1. Speech recognition

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cover image ACM Conferences

MM '24: Proceedings of the 32nd ACM International Conference on Multimedia

October 2024

11719 pages

ISBN:9798400706868

DOI:10.1145/3664647

General Chairs:
Jianfei Cai
Monash University, Australia
,
Mohan Kankanhalli
NUS, Singapore
,
Balakrishnan Prabhakaran
UT Dallas, USA
,
Susanne Boll
University of Oldenburg, Germany
,
Program Chairs:
Ramanathan Subramanian
University of Canberra & IIT Ropar, Australia
,
Liang Zheng
Australian National University, Australia
,
Vivek K. Singh
Rutgers University, USA
,
Pablo Cesar
Centrum Wiskunde & Informatica, Netherlands
,
Lexing Xie
Australian National University, Australia
,
Dong Xu
University of Hong Kong, Hong Kong

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Published: 28 October 2024

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MM '24: The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

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Cited By

Jin TYan WWang YCai SShuai QZhao ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Calibrating Prompt from History for Continual Vision-Language Retrieval and GroundingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681387(4302-4311)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681387
Yang XCheng XFu DFang MZuo JJi SZhao ZTao JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)SyncTalklip: Highly Synchronized Lip-Readable Speaker Generation with Multi-Task LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681386(8149-8158)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681386
Yan WWang YLin WGuo ZZhao ZJin TCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Low-rank Prompt Interaction for Continual Vision-Language RetrievalProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681264(8257-8266)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681264

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