Abstract

Although many previous studies have carried out multimodal learning with real-time MRI data that captures the audio-visual kinematics of the vocal tract during speech, these studies have been limited by their reliance on multi-speaker corpora. This prevents such models from learning a detailed relationship between acoustics and articulation due to considerable cross-speaker variability. In this study, we develop unimodal audio and video models as well as multimodal models for phoneme recognition using a long-form single-speaker MRI corpus, with the goal of disentangling and interpreting the contributions of each modality. Audio and multimodal models show similar performance on different phonetic manner classes but diverge on places of articulation. Interpretation of the models' latent space shows similar encoding of the phonetic space across audio and multimodal models, while the models' attention weights highlight differences in acoustic and articulatory timing for certain phonemes.

Metadata

publication

arXiv preprint arXiv:2505.24059, 2025

year

2025

publication date

2025/5/29

authors

Sean Foley, Hong Nguyen, Jihwan Lee, Sudarsana Reddy Kadiri, Dani Byrd, Louis Goldstein, Shrikanth Narayanan

link

https://arxiv.org/abs/2505.24059

resource_link

https://arxiv.org/pdf/2505.24059

journal

arXiv preprint arXiv:2505.24059