Tailored Design of Audio-Visual Speech Recognition Models using Branchformers
<|reference_start|>Tailored Design of Audio-Visual Speech Recognition Models using Branchformers: Recent advances in Audio-Visual Speech Recognition (AVSR) have led to unprecedented achievements in the field, improving the robustness of this type of system in adverse, noisy environments. In most cases, this task has been addressed through the design of models composed of two independent encoders, each dedicated to a specific modality. However, while recent works have explored unified audio-visual encoders, determining the optimal cross-modal architecture remains an ongoing challenge. Furthermore, such approaches often rely on models comprising vast amounts of parameters and high computational cost training processes. In this paper, we aim to bridge this research gap by introducing a novel audio-visual framework. Our proposed method constitutes, to the best of our knowledge, the first attempt to harness the flexibility and interpretability offered by encoder architectures, such as the Branchformer, in the design of parameter-efficient AVSR systems. To be more precise, the proposed framework consists of two steps: first, estimating audio- and video-only systems, and then designing a tailored audio-visual unified encoder based on the layer-level branch scores provided by the modality-specific models. Extensive experiments on English and Spanish AVSR benchmarks covering multiple data conditions and scenarios demonstrated the effectiveness of our proposed method. Results reflect how our tailored AVSR system is able to reach state-of-the-art recognition rates while significantly reducing the model complexity w.r.t. the prevalent approach in the field. Code and pre-trained models are available at https://github.com/david-gimeno/tailored-avsr.<|reference_end|>
@article{gimeno-gómez2024tailored,
    title={Tailored Design of Audio-Visual Speech Recognition Models using
  Branchformers},
    author={David Gimeno-G'omez and Carlos-D. Mart'inez-Hinarejos},
    journal={arXiv preprint arXiv:2407.06606},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06606},
    primaryClass={cs.CV cs.CL}
}