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Real-Time MRI Video Synthesis from Time Aligned Phonemes with Sequence-to-Sequence Networks

Udupa, S and Ghosh, PK (2023) Real-Time MRI Video Synthesis from Time Aligned Phonemes with Sequence-to-Sequence Networks. In: UNSPECIFIED.

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Official URL: https://doi.org/10.1109/ICASSP49357.2023.10094797


Real-Time Magnetic resonance imaging (rtMRI) of the midsagittal plane of the mouth is of interest for speech production research. In this work, we focus on estimating utterance level rtMRI video from the spoken phoneme sequence. We obtain time-aligned phonemes from forced alignment, to obtain frame-level phoneme sequences which are aligned with rtMRI frames. We propose a sequence-tosequence learning model with a transformer phoneme encoder and convolutional frame decoder. We then modify the learning by using intermediary features obtained from sampling from a pretrained phoneme-conditioned variational autoencoder (CVAE). We train on 8 subjects in a subject-specific manner and demonstrate the performance with a subjective test. We also use an auxiliary task of air tissue boundary (ATB) segmentation to obtain the objective scores on the proposed models. We show that the proposed method is able to generate realistic rtMRI video for unseen utterances, and adding CVAE is beneficial for learning the sequence-to-sequence mapping for subjects where the mapping is hard to learn. © 2023 IEEE.

Item Type: Conference Paper
Publication: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to author.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 04 Mar 2024 09:09
Last Modified: 04 Mar 2024 09:09
URI: https://eprints.iisc.ac.in/id/eprint/84285

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