ARTI-6: Towards Six-dimensional Articulatory Speech Encoding
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 25 Sep 2025.
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22 of 39 references.
References · 22
- 2017Attention Is All You Needcited by 60,361
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
- 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,154
- 2016Least Squares Generative Adversarial Networkscited by 1,001
- 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 628
- 2022UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022cited by 390
- 2023LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpuscited by 101
- 2022Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speechcited by 31
10 more
- 2023Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representationscited by 22
- 2021A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric imagescited by 21
- 2024Coding Speech through Vocal Tract Kinematicscited by 18
- 2023Speaker-Independent Acoustic-to-Articulatory Speech Inversioncited by 15
- 2022Deep Speech Synthesis from Articulatory Representationscited by 10
- 2025Articulatory Feature Prediction from Surface EMG during Speech Productioncited by 4
- 2025Towards disentangling the contributions of articulation and acoustics in multimodal phoneme recognitioncited by 3
- 2025Enhancing Acoustic-to-Articulatory Speech Inversion by Incorporating Nasalitycited by 2
- 2025On the Relationship between Accent Strength and Articulatory Featurescited by 2
- 2025A long-form single-speaker real-time MRI speech dataset and benchmarkcited by 2
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