ARTI-6: Towards Six-dimensional Articulatory Speech Encoding
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914279278510080 |
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| author | Lee, Jihwan Foley, Sean Lertpetchpun, Thanathai Huang, Kevin Lee, Yoonjeong Feng, Tiantian Goldstein, Louis Byrd, Dani Narayanan, Shrikanth |
| author_facet | Lee, Jihwan Foley, Sean Lertpetchpun, Thanathai Huang, Kevin Lee, Yoonjeong Feng, Tiantian Goldstein, Louis Byrd, Dani Narayanan, Shrikanth |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21447 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARTI-6: Towards Six-dimensional Articulatory Speech Encoding Lee, Jihwan Foley, Sean Lertpetchpun, Thanathai Huang, Kevin Lee, Yoonjeong Feng, Tiantian Goldstein, Louis Byrd, Dani Narayanan, Shrikanth Audio and Speech Processing Artificial Intelligence Computation and Language 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. |
| title | ARTI-6: Towards Six-dimensional Articulatory Speech Encoding |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.21447 |