Intuitive Multilingual Audio-Visual Speech Recognition with a Single-Trained Model

Joanna Hong, Se Jin Park, Yong Man Ro

Submission Type: Regular Short Paper

Submission Track: Speech and Multimodality

Submission Track 2: Multilinguality and Linguistic Diversity

Keywords: audio-visual speech recognition, speech recognition, multimodal, multilingual

TL;DR: Design a multilingual audio-visual speech recognition with a single trained model

Abstract: We present a novel approach to multilingual audio-visual speech recognition tasks by introducing a single model on a multilingual dataset. Motivated by a human cognitive system where humans can intuitively distinguish different languages without any conscious effort or guidance, we propose a model that can capture which language is given as an input speech by distinguishing the inherent similarities and differences between languages. To do so, we design a prompt fine-tuning technique into the largely pre-trained audio-visual representation model so that the network can recognize the language class as well as the speech with the corresponding language. Our work contributes to developing robust and efficient multilingual audio-visual speech recognition systems, reducing the need for language-specific models.

Submission Number: 138