Acoustic-to-articulatory Inversion based on Speech Decomposition and Auxiliary Feature

• URL: http://arxiv.org/abs/2204.00873v1
• Date: Sat, 2 Apr 2022 14:47:19 GMT
• Title: Acoustic-to-articulatory Inversion based on Speech Decomposition and Auxiliary Feature
• Authors: Jianrong Wang, Jinyu Liu, Longxuan Zhao, Shanyu Wang, Ruiguo Yu, Li Liu
• Abstract summary: We pre-train a speech decomposition network to decompose audio speech into speaker embedding and content embedding. We then propose a novel auxiliary feature network to estimate the lip auxiliary features from the personalized speech features. Experimental results show that, compared with the state-of-the-art only using the audio speech feature, the proposed method reduces the average RMSE by 0.25 and increases the average correlation coefficient by 2.0%.
• Score: 7.363994037183394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Acoustic-to-articulatory inversion (AAI) is to obtain the movement of articulators from speech signals. Until now, achieving a speaker-independent AAI remains a challenge given the limited data. Besides, most current works only use audio speech as input, causing an inevitable performance bottleneck. To solve these problems, firstly, we pre-train a speech decomposition network to decompose audio speech into speaker embedding and content embedding as the new personalized speech features to adapt to the speaker-independent case. Secondly, to further improve the AAI, we propose a novel auxiliary feature network to estimate the lip auxiliary features from the above personalized speech features. Experimental results on three public datasets show that, compared with the state-of-the-art only using the audio speech feature, the proposed method reduces the average RMSE by 0.25 and increases the average correlation coefficient by 2.0% in the speaker-dependent case. More importantly, the average RMSE decreases by 0.29 and the average correlation coefficient increases by 5.0% in the speaker-independent case.

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