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Determining an Optimal Set of Flesh Points on Tongue, Lips, and Jaw for Continuous Silent Speech Recognition.

, , and . SLPAT@Interspeech, page 79-85. Association for Computational Linguistics, (2015)

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Speaker-Independent Silent Speech Recognition From Flesh-Point Articulatory Movements Using an LSTM Neural Network., , , and . IEEE ACM Trans. Audio Speech Lang. Process., 25 (12): 2323-2336 (2017)Tongue and Lip Motion Patterns in Alaryngeal Speech., , , , , , and . INTERSPEECH, page 4576-4580. ISCA, (2020)Determining an Optimal Set of Flesh Points on Tongue, Lips, and Jaw for Continuous Silent Speech Recognition., , and . SLPAT@Interspeech, page 79-85. Association for Computational Linguistics, (2015)Multiview Representation Learning via Deep CCA for Silent Speech Recognition., , , and . INTERSPEECH, page 2769-2773. ISCA, (2017)Investigating Speech Reconstruction for Laryngectomees for Silent Speech Interfaces., , , , , , and . Interspeech, page 651-655. ISCA, (2021)Permanent Magnetic Articulograph (PMA) vs Electromagnetic Articulograph (EMA) in Articulation-to-Speech Synthesis for Silent Speech Interface., , , , and . SLPAT@NAACL-HLT, page 17-23. Association for Computational Linguistics, (2019)Integrating Articulatory Information in Deep Learning-Based Text-to-Speech Synthesis., , , , and . INTERSPEECH, page 254-258. ISCA, (2017)Articulation-to-Speech Synthesis Using Articulatory Flesh Point Sensors' Orientation Information., , , , , and . INTERSPEECH, page 3152-3156. ISCA, (2018)Data Augmentation for End-to-end Silent Speech Recognition for Laryngectomees., , , , , , , and . INTERSPEECH, page 3653-3657. ISCA, (2022)