請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99502完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 楊燿州 | zh_TW |
| dc.contributor.advisor | Yao-Joe Yang | en |
| dc.contributor.author | 林聖凱 | zh_TW |
| dc.contributor.author | Sheng-Kai Lin | en |
| dc.date.accessioned | 2025-09-10T16:29:18Z | - |
| dc.date.available | 2025-09-11 | - |
| dc.date.copyright | 2025-09-10 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-28 | - |
| dc.identifier.citation | [1] J. A. Gonzalez-Lopez, A. Gomez-Alanis, J. M. M. Doñas, J. L. Pérez-Córdoba, and A. M. Gomez, “Silent speech interfaces for speech restoration: A review,” IEEE Access, vol. 8, pp. 177995–178021, 2020.
[2] N. Roy, S. Merrill, S. Thibeault, L. Parsa, and J. M. Gray, “Voice disorders in the general population: prevalence, risk factors, and occupational impact,” The Laryngoscope, vol. 115, no. 11, pp. 1988–1995, 2005. [3] A. Karjalainen and D. Dupré, “Statistics in Focus: Population and social conditions. Employment of disabled people in Europe in 2002. 2003.26,” 2003. [4] W. H. Organization, “World report on disability 2011.” 2011: World Health Organization. [5] J. Murray and J. Goldbart, “Augmentative and alternative communication: a review of current issues,” Paediatrics and Child Health, vol. 19, no. 10, pp. 464–468, 2009. [6] B. Denby, T. Schultz, K. Honda, T. Hueber, J. M. Gilbert, and J. S. Brumberg, “Silent speech interfaces,” Speech Communication, vol. 52, no. 4, pp. 270–287, 2010. [7] A. Moin, A. Zhou, A. Rahimi, A. Menon, S. Benatti, A. Alexandrov, W. Burleson, J. Tam, F. Yamamoto, Y. Khan, and A. C. Arias, “A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition,” Nature Electronics, vol. 4, pp. 54–63, 2021. [8] Z. Zhou, Y. Dai, K. Yao, Y. Liu, L. Li, Q. Zhao, J. Li, Q. Pei, and X. Chen, “Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays,” Nature Electronics, vol. 3, pp. 571–578, 2020. [9] B. Denby, S. Chen, Y. Zheng, K. Xu, Y. Yang, C. Leboullenger, and P. Roussel, “Recent results in silent speech interfaces,” J. Acoust. Soc. Amer., vol. 141, no. 5, p. 3646, 2017. [10] J. S. Chung, A. Senior, O. Vinyals, and A. Zisserman, “Lip reading sentences in the wild,” Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3444–3453, 2017. [11] H. Akbari, H. Arora, L. Cao, and N. Mesgarani, “Lip2AudSpec: Speech reconstruction from silent lip movements video,” Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2516–2520, 2018. [12] A. Pass, J. Zhang, and D. Stewart, “An investigation into features for multi-view lipreading,” Proceedings of the 2010 IEEE International Conference on Image Processing (ICIP), pp. 2417–2420, 2010. [13] M. J. Fagan, S. R. Ell, J. M. Gilbert, E. Sarrazin, and P. M. Chapman, “Development of a (silent) speech recognition system for patients following laryngectomy,” Medical Engineering & Physics, vol. 30, no. 4, pp. 419–425, 2008. [14] G. K. Anumanchipalli, J. Chartier, and E. F. Chang, “Speech synthesis from neural decoding of spoken sentences,” Nature, vol. 568, pp. 493–498, 2019. [15] B. Martinez, P. Ma, S. Petridis, and M. Pantic, “Lipreading using temporal convolutional networks,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6319–6323, 2020. [16] M. Rothenberg, “A multichannel electroglottograph,” Journal of Voice, vol. 6, no. 1, pp. 36–43, 1992. [17] C. Herff, D. Heger, A. De Pesters, D. Telaar, P. Brunner, G. Schalk, and T. Schultz, “Brain-to-text: decoding spoken phrases from phone representations in the brain,” Frontiers in Neuroscience, vol. 9, p. 217, 2015. [18] C. Jorgensen, D. D. Lee, and S. Agabont, “Sub auditory speech recognition based on EMG signals,” Proceedings of the International Joint Conference on Neural Networks, vol. 4, pp. 3128–3133, 2003. [19] L. Maier-Hein, F. Metze, T. Schultz, and A. Waibel, “Session independent non-audible speech recognition using surface electromyography,” in IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 331–336, 2005. [20] N. Hashim, A. Ali, and W. N. Mohd-Isa, “Word-based classification of imagined speech using EEG,” in International Conference on Computational Science and Technology, pp. 195–204, Springer Singapore, 2017. [21] C. H. Nguyen, G. K. Karavas, and P. Artemiadis, “Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features,” Journal of Neural Engineering, vol. 15, no. 1, p. 016002, 2017. [22] K. W. Lee, D. H. Lee, S. J. Kim, and S. W. Lee, “Decoding neural correlation of language-specific imagined speech using EEG signals,” in 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1977–1980, 2022. [23] M. Angrick, C. Herff, E. Mugler, M. C. Tate, M. W. Slutzky, D. J. Krusienski, and T. Schultz, “Speech synthesis from ECoG using densely connected 3D convolutional neural networks,” Journal of Neural Engineering, vol. 16, no. 3, p. 036019, 2019. [24] T. Proix, J. Delgado Saa, A. Christen, S. Martin, B. N. Pasley, R. T. Knight, and A. L. Giraud, “Imagined speech can be decoded from low-and cross-frequency intracranial EEG features,” Nature Communications, vol. 13, no. 1, p. 48, 2022. [25] K. Lin, W. Hong, C. Huang, Y. Su, S. Zhang, C. Wang, and J. Song, “Stretchable high-density surface electromyography electrode patch assisted with machine learning for silent speech recognition,” The European Physical Journal Special Topics, pp. 1–9, 2025. [26] C. Tang, J. Mallah, D. Kazieczko, W. Yi, T. R. Kandukuri, E. Occhipinti, and L. G. Occhipinti, “Wireless silent speech interface using multi-channel textile EMG sensors integrated into headphones,” arXiv preprint arXiv:2504.13921, 2025. [27] B. Zhu, X. Zeng, Z. Chen, D. Zhang, and L. Xie, “SSR using portable sEMG acquisition system with electrode layout optimization,” IEEE Sensors Journal, 2024. [28] P. Dong, S. Tian, S. Chen, Y. Li, S. Li, M. Zheng, and S. Yao, “Decoding silent speech cues from muscular biopotential signals for efficient human-robot collaborations,” Advanced Materials Technologies, vol. 10, no. 4, p. 2400990, 2025. [29] H. Tian, X. Li, Y. Wei, S. Ji, Q. Yang, G. Y. Gou, and T. L. Ren, “Bioinspired dual-channel speech recognition using graphene-based electromyographic and mechanical sensors,” Cell Reports Physical Science, vol. 3, no. 10, 2022. [30] J. S. Kang, K. S. Moon, S. Q. Lee, N. Satterlee, and X. Zuo, “A wearable silent text input system using EMG and piezoelectric sensors,” Sensors, vol. 25, no. 8, p. 2624, 2025. [31] B. Huang, Y. Shao, H. Zhang, P. Wang, X. Chen, Z. Li, and B. Han, “Design and implementation of a silent speech recognition system based on sEMG signals: A neural network approach,” Biomedical Signal Processing and Control, vol. 92, p. 106052, 2024. [32] A. T. Chowdhury, M. Newaz, P. Saha, M. N. AbuHaweeleh, S. Mohsen, D. Bushnaq, and M. E. Chowdhury, “Decoding silent speech: a machine learning perspective on data, methods, and frameworks,” Neural Computing and Applications, pp. 1–19, 2025. [33] D. Gaddy and D. Klein, “Digital voicing of silent speech,” arXiv preprint arXiv:2010.02960, 2020. [34] R. Song, X. Zhang, X. Chen, X. Chen, X. Chen, S. Yang, and E. Yin, “Decoding silent speech from high-density surface electromyographic data using transformer,” Biomedical Signal Processing and Control, vol. 80, p. 104298, 2023. [35] M. Orken, O. Dina, A. Keylan, T. Tolganay, and O. Mohamed, “A study of transformer-based end-to-end speech recognition system for Kazakh language,” Scientific Reports, vol. 12, no. 1, p. 8337, 2022. [36] S. H. Wright, “Generation of resting membrane potential,” Advances in Physiology Education, vol. 28, no. 4, pp. 139–142, 2004. [37] Medical Gallery of Blausen Medical, 2014. [38] Labster, “Action potential,” Available: https://theory.labster.com/action_potential [39] C. L. Ng and M. B. I. Reaz, “Evolution of a capacitive electromyography contactless biosensor: Design and modelling techniques,” Measurement, vol. 145, pp. 460–471, 2019. [40] L. S. Hsu, S. W. Tung, C. H. Kuo, and Y. J. Yang, “Developing barbed microtip-based electrode arrays for biopotential measurement,” Sensors, vol. 14, no. 7, pp. 12370–12386, 2014. [41] M. Kim, T. Kim, D. S. Kim, and W. K. Chung, “Curved microneedle array-based sEMG electrode for robust long-term measurements and high selectivity,” Sensors, vol. 15, no. 7, pp. 16265–16280, 2015. [42] K. Watkins and T. Paus, “Modulation of motor excitability during speech perception: the role of Broca's area,” Journal of Cognitive Neuroscience, vol. 16, no. 6, pp. 978–987, 2004. [43] W. Li, J. Yuan, L. Zhang, J. Cui, X. Wang, and H. Li, “sEMG-based technology for silent voice recognition,” Computers in Biology and Medicine, vol. 152, p. 106336, 2023. [44] A. Kapur, U. Sarawgi, E. Wadkins, M. Wu, N. Hollenstein, and P. Maes, "Non-invasive silent speech recognition in multiple sclerosis with dysphonia," Machine Learning for Health Workshop, PMLR, 2020. [45] Ministry of Education Taiwan, “Special Education Portal,” Available: https://special.moe.gov.tw/. [46] Y. Joo, et al., “Highly sensitive and bendable capacitive pressure sensor and its application to 1V operation pressure‐sensitive transistor,” *Advanced Electronic Materials*, vol. 3, no. 4, 2017. [47] S. Lee, et al., “A transparent bending insensitive pressure sensor,” Nature Nanotechnology, vol. 11, no. 5, pp. 472–478, 2016. [48] N. N. Jason, et al., “Resistive electronic skin,” Journal of Materials Chemistry C, vol. 5, no. 24, pp. 5845–5866, 2017. [49] S. Gong, et al., “One‐Dimensional Nanomaterials for Soft Electronics,” Advanced Electronic Materials, vol. 3, no. 3, 2017. [50] T. Q. Trung, et al., “Flexible and stretchable physical sensor integrated platforms for wearable human‐activity monitoring and personal healthcare,” Advanced Materials, vol. 28, no. 22, pp. 4338–4372, 2016. [51] H. Yousef, et al., “Tactile sensing for dexterous in-hand manipulation in robotics-A review,” Sensors and Actuators A: physical, vol. 167, no. 2, pp. 171-187, 2011. [52] H. Deng, et al., “Progress on the morphological control of conductive network in conductive polymer composites and the use as electroactive multifunctional materials,” Progress in Polymer Science, vol. 39, no. 4, pp. 627-655, 2014. [53] S. Yao, et al., “A wearable hydration sensor with conformal nanowire electrodes,” Advanced healthcare materials, vol. 6, no. 6, 2017. [54] J. Chen, et al., “Balance the electrical properties and mechanical properties of carbon black filled immiscible polymer blends with a double percolation structure,” Composites Science and Technology, vol. 140, pp. 99-105, 2017. [55] Z. Zhan, et al., “Ultrahigh surface‐enhanced Raman scattering of graphene from Au/Graphene/Au sandwiched structures with subnanometer gap,” Advanced Optical Materials, vol. 4, no. 12, pp. 2021-2027, 2016. [56] Y. Ai, et al., “All rGO-on-PVDF-nanofibers based self-powered electronic skins,” Nano Energy, vol. 35, pp. 121-127, 2017. [57] J. Park, et al., “Tailoring force sensitivity and selectivity by microstructure engineering of multidirectional electronic skins,” NPG Asia Materials, vol. 10, pp. 167-176, 2018. [58] K. Y. Shin, et al., “Implication of size-controlled graphite nanosheets as building blocks for thermal conductive three-dimensional framework architecture of nanocarbons,” Nanoscale and Microscale Thermophysical Engineering, vol. 22, no. 1, pp. 39-51, 2018. [59] H. Liu, et al., “Lightweight conductive graphene/thermoplastic polyurethane foams with ultrahigh compressibility for piezoresistive sensing,” Journal of Materials Chemistry C, vol. 5, no. 1, pp. 73-83, 2017. [60] W. Bauhofer, et al., “A review and analysis of electrical percolation in carbon nanotube polymer composites,” Composites Science and Technology, vol. 69, no. 10, pp. 1486-1498, 2009. [61] C. Armbruster, et al., “Characteristics of highly flexible PDMS membranes for long‐term mechanostimulation of biological tissue,” Journal of Biomedical Materials Research Part B: Applied Biomaterials: An Official Journal of the Society for Biomaterials, The Japanese Society for Biomaterials, and The Australian Society for Biomaterials and the Korean Society for Biomaterials, vol. 91, no. 2, pp. 700-705, 2009. [62] S. Wu, et al., “Novel electrically conductive porous PDMS/carbon nanofiber composites for deformable strain sensors and conductors,” ACS applied materials & interfaces, vol. 9, no. 16, pp. 14207-14215, 2017. [63] N. Hu, et al., “Tunneling effect in a polymer/carbon nanotube nanocomposite strain sensor,” Acta Materialia, vol. 56, no.13, pp. 2929-2936, 2008. [64] G.R. Ruschau, et al., “Resistivities of conductive composites,” Journal of applied physics, vol. 72, no. 3, pp. 953-959, 1992. [65] R. Taherian, “Development of an equation to model electrical conductivity of polymer-based carbon nanocomposites,” ECS Journal of Solid State Science and Technology, vol. 3, no. 6, pp. 26-38, 2014. [66] Y. L. Park, et al., “Giant tunneling piezoresistance of composite elastomers with interlocked microdome arrays for ultrasensitive and multimodal electronic skins,” ACS Nano, vol. 8, no. 5, pp. 4689-4697, 2014. [67] C. J. Lee and J. I. Song, “A chopper stabilized current-feedback instrumentation amplifier for EEG acquisition applications,” IEEE Access, vol. 7, pp. 11565–11569, 2019. [68] P. Naktongkul and A. Thanachayanont, “1.5-V 900-μW 40-dB CMOS variable gain amplifier,” in 2006 IEEE International Symposium on Circuits and Systems, pp. 4 pp., 2006. [69] N. Kim, T. Lim, K. Song, S. Yang, and J. Lee, “Stretchable multichannel electromyography sensor array covering large area for controlling home electronics with distinguishable signals from multiple muscles,” ACS Applied Materials & Interfaces, vol. 8, no. 32, pp. 21070–21076, 2016. [70] T. Kim, Y. Shin, K. Kang, K. Kim, G. Kim, Y. Byeon, and K. J. Yu, “Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces,” Nature Communications, vol. 13, no. 1, p. 5815, 2022. [71] R. W. Schafer, “What is a Savitzky-Golay filter? \\[lecture notes],” IEEE Signal Processing Magazine, vol. 28, no. 4, pp. 111–117, 2011. [72] S. C. S. Jou, T. Schultz, M. Walliczek, F. Kraft, and A. Waibel, “Towards continuous speech recognition using surface electromyography,” in Interspeech, pp. 573–576, 2006. [73] R. Collobert, C. Puhrsch, and G. Synnaeve, “Wav2letter: an end-to-end convnet-based speech recognition system,” arXiv preprint arXiv:1609.03193, 2016. [74] S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” *arXiv preprint arXiv:1904.05862*, 2019. [75] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., “Attention is all you need,” in Advances in Neural Information Processing Systems, vol. 30, 2017. [76] A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in Proceedings of the 23rd International Conference on Machine Learning, pp. 369–376, 2006. [77] C. Amrhein and R. Sennrich, “On Romanization for model transfer between scripts in neural machine translation,” arXiv preprint arXiv:2009.14824, 2020. [78] S. F. Chen and J. Goodman, “An empirical study of smoothing techniques for language modeling,” Computer Speech & Language, vol. 13, no. 4, pp. 359–394, 1999. [79] L. Xie, Y. Zhang, H. Yuan, M. Zhang, X. Zhang, C. Zheng, et al., “Neural Chinese silent speech recognition with facial electromyography,” Speech Communication, vol. 171, p. 103230, 2025. [80] H. Seki, T. Hori, S. Watanabe, N. Moritz, and J. Le Roux, “Vectorized beam search for CTC-attention-based speech recognition,” in INTERSPEECH, pp. 3825–3829, 2019. [81] I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101, 2017. [82] S. Heckman and L. Williams, “A model building process for identifying actionable static analysis alerts,” in 2009 International Conference on Software Testing Verification and Validation, pp. 161–170, 2009. [83] D. Gaddy and D. Klein, “An improved model for voicing silent speech,” arXiv preprint arXiv:2106.01933, 2021. [84] C. Tang, M. Xu, W. Yi, Z. Zhang, E. Occhipinti, C. Dong, et al., “Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency,” npj Flexible Electronics, vol. 8, no. 1, p. 27, 2024. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99502 | - |
| dc.description.abstract | 無聲語音介面（SSI）能在無法獲取語音訊號的情況下辨識口語發音，並可作為語音障礙患者的溝通翻譯工具。本研究針對語言障礙患者的溝通需求，開發了一套人工智慧無聲話語介面系統。該系統結合可穿戴式感測裝置與人工智慧無聲話語辨識模型，能夠擷取人體面部的肌肉電訊號（EMG），並透過導電高分子應變感測器量測穿戴式支架上的彎曲應變。在系統訊號轉換上，所獲取的肌電和應變訊號首先會先進行前處理，隨後利用卷積神經網路（CNN）進行特徵萃取，捕捉原始訊號中的時域與頻域特徵。接著，這些特徵被輸入至Transformer神經網路，用以建立訊號序列與語意之間的對應關係，將非聲學的訊號轉換為對應的詞彙特徵。最後，系統整合連接主義時序分類(CTC)演算法及n-gram語言模型，進行更精確的詞彙特徵解碼與語句預測，實現中英文語音的文字輸出。系統於中英文多組常用詞彙上進行驗證，分別於英文796筆與中文592筆詞彙下達到8.6%與13.11%的文字錯誤率（WER），多組常用詞彙辨識準確率亦均超過95%。整體而言，本研究所提出的AI無聲話語介面系統展現出優異的跨語言辨識效能，不僅為語言障礙者或特殊環境下的無聲話語溝通提供創新解決方案，更具備推廣至智慧醫療及人機互動等領域的高度應用潛力。 | zh_TW |
| dc.description.abstract | Silent speech interfaces (SSIs) enable recognition of verbal expressions from non-acoustic biosignals generated by facial movements, offering a promising solution for people with voice disorders or difficulties in verbal communication. In this work, we present a wearable silent speech interface (SSI) system integrating electromyogram (EMG) sensing electrodes with a conductive polymer-based strain sensor. An AI speech recognition model processes these signals to enable assisted speaking without relying on vocal fold activation. After preprocessing the acquired EMG and strain signals for obtaining high-quality input for recognition, features are extracted using a convolutional neural network (CNN), and a transformer architecture is utilized to aggregate contextual features into meaningful word features. The feature outputs are passed through a connectionist temporal classification (CTC) decoder, which aligns the predicted sequences with target sentence labels and, combined with an n-gram language model, accurately generates the final word sequences for both Chinese and English words. The AI speech recognition model achieved excellent word error rates (WER) of 8.6% and 13.11% on datasets containing 796 English and 592 Chinese words, demonstrating high recognition accuracy (>95%) across various datasets of commonly used vocabulary. This proposed wearable silent speech interface (SSI) potentially helps people with vocal cord injuries regain their ability to speak and enables effective communication in special situations and environments. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-10T16:29:18Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-10T16:29:18Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目次
論文審定書 I 致謝 III 摘要 V Abstract VII 目次 IX 圖次 XIII 表次 XVII 符號說明 XIX 第一章 緒論 1 1.1前言 1 1.2文獻回顧 2 1.2.1 視覺監測式無聲話語介面 2 1.2.2 非視覺監測式無聲話語介面 5 1.2.3 雙通道語音識別無聲話語介面技術 19 1.2.4 無聲話語介面結合人工智慧之應用 22 1.3研究動機與目的 27 1.4論文架構 29 第二章 研究理論基礎 31 2.1本章介紹 31 2.2 人體臉部肌電訊號之特徵點位置分析 31 2.2.1肌電圖基本原理 31 2.2.2皮膚結構和等效電路模型 35 2.2.3面部發聲特徵點擷取 36 2.2.4預測目標語料庫建立 37 2.3 導電高分子應變感測器之感測機制與材料選用 37 2.3.1導電高分子 38 2.3.2導電粒子 39 2.3.3高分子基材 39 2.3.4導電高分子之導電機制 40 2.4 人體發聲組織訊號預處理方法 43 2.4.1 訊號增益 44 2.4.2 訊號濾波 44 2.5人工智慧無聲話語辨識平台原理 46 2.5.1特徵萃取(Feature Extraction) 46 2.5.2 Transformer神經網路 50 2.5.3 連接主義時序分類（CTC） 55 2.5.4 N-gram語言模型 58 2.5.5 AI模型訓練與評估方法 60 第三章 系統裝置與製造方法 63 3.1 本章介紹 63 3.2 導電高分子應變感測器設計與製造方法 63 3.2.1 導電高分子之製備 65 3.2.2導電高分子模具製作 67 3.2.3 光罩設計 68 3.2.4 微影製程 69 3.2.5 元件製程結果 74 3.3頭套設計 76 3.4 人工智慧無聲話語辨識模型架構設計 79 3.4.1訊號資料預處理 80 3.4.2 CNN 特徵萃取 81 3.4.3 神經網路訊號轉換 82 第四章 量測結果與討論 84 4.1 本章介紹 84 4.2 量測系統 84 4.3 量測結果與討論 88 4.3.1導電高分子應變感測器之量測結果 88 4.3.2人體發聲組織訊號之量測結果 89 4.3.3模型字錯率(WER)量測結果 91 4.3.4混淆矩陣 96 第五章 結論與未來展望 99 5.1結論 99 5.2未來展望 100 參考文獻 103 附錄A 111 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 無聲話語介面 | zh_TW |
| dc.subject | 人工智慧無聲話語辨識模型 | zh_TW |
| dc.subject | 導電高分子應變感測器 | zh_TW |
| dc.subject | 面部肌電圖 | zh_TW |
| dc.subject | Transformer | zh_TW |
| dc.subject | 連接主義時序分類（CTC) | zh_TW |
| dc.subject | Conductive-polymer-based strain sensor | en |
| dc.subject | Silent speech interfaces | en |
| dc.subject | Connectionist Temporal Classification | en |
| dc.subject | Transformer | en |
| dc.subject | Facial electromyography | en |
| dc.subject | AI silent speech recognition model | en |
| dc.title | 無聲話語介面裝置之人工智慧模型的開發 | zh_TW |
| dc.title | Development of an AI Recognition Model for Silent Speech Interfaces | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳國聲;蘇裕軒 | zh_TW |
| dc.contributor.oralexamcommittee | Kuo-Shen Chen;Yu-Hsuan Su | en |
| dc.subject.keyword | 無聲話語介面,人工智慧無聲話語辨識模型,導電高分子應變感測器,面部肌電圖,Transformer,連接主義時序分類（CTC), | zh_TW |
| dc.subject.keyword | Silent speech interfaces,AI silent speech recognition model,Conductive-polymer-based strain sensor,Facial electromyography,Transformer,Connectionist Temporal Classification, | en |
| dc.relation.page | 113 | - |
| dc.identifier.doi | 10.6342/NTU202502206 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-07-29 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 機械工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf 未授權公開取用 | 6.46 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。