ARTI-6: Towards Six-dimensional Articulatory Speech Encoding

Jihwan Lee; Sean Foley; Thanathai Lertpetchpun; Kevin Huang; Yoonjeong Lee; Tiantian Feng; Louis Goldstein; Dani Byrd; Shrikanth Narayanan

arXiv · 2509.21447

ARTI-6: Towards Six-dimensional Articulatory Speech Encoding

Jihwan Lee·Sean Foley·Thanathai Lertpetchpun·Kevin Huang·Yoonjeong Lee·Tiantian Feng·Louis Goldstein·Dani Byrd·Shrikanth Narayanan

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Abstract

We propose ARTI-6, a compact six-dimensional articulatory speech encoding framework derived from real-time MRI data that captures crucial vocal tract regions including the velum, tongue root, and larynx. ARTI-6 consists of three components: (1) a six-dimensional articulatory feature set representing key regions of the vocal tract; (2) an articulatory inversion model, which predicts articulatory features from speech acoustics leveraging speech foundation models, achieving a prediction correlation of 0.87; and (3) an articulatory synthesis model, which reconstructs intelligible speech directly from articulatory features, showing that even a low-dimensional representation can generate natural-sounding speech. Together, ARTI-6 provides an interpretable, computationally efficient, and physiologically grounded framework for advancing articulatory inversion, synthesis, and broader speech technology applications. The source code and speech samples are publicly available.

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Jihwan Lee, Sean Foley, Thanathai Lertpetchpun, Kevin Huang, Yoonjeong Lee, Tiantian Feng, Louis Goldstein, Dani Byrd, Shrikanth Narayanan. 2025-09-25. ARTI-6: Towards Six-dimensional Articulatory Speech Encoding. https://arxiv.org/abs/2509.21447

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