@inproceedings{
zheng2024speech,
title={Speech Reconstruction from Silent Lip and Tongue Articulation by Diffusion Models and Text-Guided Pseudo Target Generation},
author={Rui-Chen Zheng and Yang Ai and Zhen-Hua Ling},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=M87zOOryOG}
}
This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing real speech. 
This task falls under the umbrella of articulatory-to-acoustic (A2A) conversion and may also be referred to as a silent speech interface. 
To overcome the domain discrepancy between silent and standard vocalized articulation, we introduce a novel pseudo target generation strategy. 
It integrates the text modality to align with articulatory movements, thereby guiding the generation of pseudo acoustic features for supervised training on speech reconstruction from silent articulation.
Furthermore, we propose to employ a denoising diffusion probabilistic model as the fundamental architecture for the A2A conversion task and train the model using a combined training approach with the generated pseudo acoustic features.
Experiments show that our proposed method significantly improves the intelligibility and naturalness of the reconstructed speech in the silent speaking mode compared to all baseline methods. 
Specifically, the word error rate of the reconstructed speech decreases by approximately 5\% when measured using an automatic speech recognition engine for intelligibility assessment, and the subjective mean opinion score for naturalness improves by 0.14. 
Moreover, analytical experiments reveal that the proposed pseudo target generation strategy can generate pseudo acoustic features that synchronize better with articulatory movements than previous strategies.
Samples are available at our project page.
Speech Reconstruction from Silent Lip and Tongue Articulation by Diffusion Models and Text-Guided Pseudo Target Generation
Rui-Chen Zheng Yang Ai Zhen-Hua Ling