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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李宏毅 | zh_TW |
dc.contributor.advisor | Hung-Yi Lee | en |
dc.contributor.author | 陳柏文 | zh_TW |
dc.contributor.author | Bo-Wen Chen | en |
dc.date.accessioned | 2023-01-08T17:04:44Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-01-06 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2022-11-07 | - |
dc.identifier.citation | M. Johnson, M. Schuster, Q. V. Le, M. Krikun, Y. Wu, Z. Chen, N. Thorat, F. Viégas, M. Wattenberg, G. Corrado, M. Hughes, and J. Dean, “Google’s multilingual neural machine translation system: Enabling zero-shot translation,” Transactions of the Association for Computational Linguistics, vol. 5, pp. 339–351, 2017. [Online]. Available: https://www.aclweb.org/anthology/Q17-1024
R. Aharoni, M. Johnson, and O. Firat, “Massively multilingual neural machine translation,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 3874–3884. [Online]. Available: https://aclanthology.org/N19-1388 B. Zhang, A. Bapna, R. Sennrich, and O. Firat, “Share or not? learning to schedule language-specific capacity for multilingual translation,” in International Conference on Learning Representations, 2021. [Online]. Available: https://openreview.net/forum?id=Wj4ODo0uyCF W. Chan, N. Kitaev, K. Guu, M. Stern, and J. Uszkoreit, “KERMIT: generative insertion-based modeling for sequences,” CoRR, vol. abs/1906.01604, 2019. [Online]. Available: http://arxiv.org/abs/1906.01604 D. He, Y. Xia, T. Qin, L. Wang, N. Yu, T.-Y. Liu, and W.-Y. Ma, “Dual learning for machine translation,” in Proceedings of the 30th International Conference on Neural Information Processing Systems, ser. NIPS’16. Red Hook, NY, USA: Curran Associates Inc., 2016, p. 820–828. R. Sennrich, B. Haddow, and A. Birch, “Improving neural machine translation models with monolingual data,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Berlin, Germany: Association for Computational Linguistics, Aug. 2016, pp. 86–96. [Online]. Available: https://aclanthology.org/P16-1009 A. Tjandra, S. Sakti, and S. Nakamura, “Listening while speaking: Speech chain by deep learning,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2017, pp. 301–308. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,”in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Computation, vol. 1, no. 4, pp. 541–551, 1989. F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1800–1807. D. E. Rumelhart and J. L. McClelland, Learning Internal Representations by Error Propagation, 1987, pp. 318–362. K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder–decoder for statistical machine translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics, Oct. 2014, pp. 1724–1734. [Online]. Available: https://www.aclweb.org/anthology/D14-1179 S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf R. Xiong, Y. Yang, D. He, K. Zheng, S. Zheng, C. Xing, H. Zhang, Y. Lan, L. Wang, and T. Liu, “On layer normalization in the transformer architecture,” in Proceedings of the 37th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 13–18 Jul 2020, pp. 10 524–10 533. [Online]. Available:http://proceedings.mlr.press/v119/xiong20b.html P. Shaw, J. Uszkoreit, and A. Vaswani, “Self-attention with relative position representations,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 464–468. [Online]. Available: https://www.aclweb.org/anthology/N18-2074 Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. Le, and R. Salakhutdinov, “TransformerXL: Attentive language models beyond a fixed-length context,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, Jul. 2019, pp. 2978–2988. [Online]. Available: https://www.aclweb.org/anthology/P19-1285 A. Gulati, J. Qin, C. Chiu, N. Parmar, Y. Zhang, J. Yu, W. Han, S. Wang, Z. Zhang, Y. Wu, and R. Pang, “Conformer: Convolution-augmented transformer for speech recognition,” in Interspeech 2020, 21st Annual Conference of the International Speech Communication Association, Virtual Event, Shanghai, China, 25-29 October 2020, H. Meng, B. Xu, and T. F. Zheng, Eds. ISCA, 2020, pp. 5036–5040. [Online]. Available: https://doi.org/10.21437/Interspeech.2020-3015 P. Guo, F. Boyer, X. Chang, T. Hayashi, Y. Higuchi, H. Inaguma, N. Kamo, C. Li, D. Garcia-Romero, J. Shi, J. Shi, S. Watanabe, K. Wei, W. Zhang, and Y. Zhang, “Recent developments on espnet toolkit boosted by conformer,” 2020. I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in Neural Information Processing Systems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger, Eds., vol. 27. Curran Associates, Inc., 2014. [Online]. Available: https://proceedings.neurips.cc/paper/2014/file/a14ac55a4f27472c5d894ec1c3c743d2-Paper.pdf J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin, “Convolutional sequence to sequence learning,” in Proceedings of the 34th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70. PMLR, 06–11 Aug 2017, pp. 1243–1252. [Online]. Available: http://proceedings.mlr.press/v70/gehring17a.html J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan, R. A. Saurous, Y. Agiomvrgiannakis, and Y. Wu, “Natural tts synthesis by conditioning wavenet on mel spectrogram predictions,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 4779–4783. J. Libovický and J. Helcl, “End-to-end non-autoregressive neural machine translation with connectionist temporal classification,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 3016–3021. [Online]. Available: https://www.aclweb.org/anthology/D18-1336 C. Saharia, W. Chan, S. Saxena, and M. Norouzi, “Non-autoregressive machine translation with latent alignments,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Online: Association for Computational Linguistics, Nov. 2020, pp. 1098–1108. [Online]. Available: https://www.aclweb.org/anthology/2020.emnlp-main.83 Y. Higuchi, S. Watanabe, N. Chen, T. Ogawa, and T. Kobayashi, “Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict,” in Proc. Interspeech 2020, 2020, pp. 3655–3659. [Online]. Available: http://dx.doi.org/10.21437/Interspeech.2020-2404 Y. Higuchi, H. Inaguma, S. Watanabe, T. Ogawa, and T. Kobayashi, “Improved maskctc for non-autoregressive end-to-end asr,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 8363–8367. Y. Ren, Y. Ruan, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech: Fast, robust and controllable text to speech,” in Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019. [Online]. Available: https://proceedings.neurips.cc/paper/2019/file/f63f65b503e22cb970527f23c9ad7db1-Paper.pdf Y. Ren, C. Hu, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech 2: Fast and high-quality end-to-end text to speech,” in International Conference on Learning Representations, 2021. [Online]. Available: https://openreview.net/forum?id=piLPYqxtWuA L. Dinh, D. Krueger, and Y. Bengio, “NICE: non-linear independent components estimation,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Workshop Track Proceedings, Y. Bengio and Y. LeCun, Eds., 2015. [Online]. Available: http://arxiv.org/abs/1410.8516 L. Dinh, J. Sohl-Dickstein, and S. Bengio, “Density estimation using real NVP,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017. [Online]. Available: https://openreview.net/forum?id=HkpbnH9lx D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” in Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., vol. 31. Curran Associates, Inc., 2018. [Online]. Available: https://proceedings.neurips.cc/paper/2018/file/d139db6a236200b21cc7f752979132d0-Paper.pdf R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 3617–3621. J. Kim, S. Kim, J. Kong, and S. Yoon, “Glow-tts: A generative flow for text-to-speech via monotonic alignment search,” in Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 8067–8077. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/5c3b99e8f92532e5ad1556e53ceea00c-Paper.pdf X. Ma, C. Zhou, X. Li, G. Neubig, and E. Hovy, “FlowSeq: Non-autoregressive conditional sequence generation with generative flow,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong, China: Association for Computational Linguistics, Nov. 2019, pp. 4282–4292. [Online]. Available: https://www.aclweb.org/anthology/D19-1437 D. Rezende and S. Mohamed, “Variational inference with normalizing flows,” in Proceedings of the 32nd International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, F. Bach and D. Blei, Eds., vol. 37. Lille, France: PMLR, 07–09 Jul 2015, pp. 1530–1538. [Online]. Available: http://proceedings.mlr.press/v37/rezende15.html I. Kobyzev, S. Prince, and M. Brubaker, “Normalizing flows: An introduction and review of current methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2020. A. N. Gomez, M. Ren, R. Urtasun, and R. B. Grosse, “The reversible residual network: Backpropagation without storing activations,” in Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/f9be311e65d81a9ad8150a60844bb94c-Paper.pdf N. Kitaev, L. Kaiser, and A. Levskaya, “Reformer: The efficient transformer,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. [Online]. Available: https://openreview.net/forum?id=rkgNKkHtvB H. R. Ihm, J. Y. Lee, B. J. Choi, S. J. Cheon, and N. S. Kim, “Reformertts: Neural speech synthesis with reformer network,” in Interspeech 2020, 21st Annual Conference of the International Speech Communication Association, Virtual Event, Shanghai, China, 25-29 October 2020, H. Meng, B. Xu, and T. F. Zheng, Eds. ISCA, 2020, pp. 2012–2016. [Online]. Available: https://doi.org/10.21437/Interspeech.2020-2189 B. Sisman, J. Yamagishi, S. King, and H. Li, “An overview of voice conversion and its challenges: From statistical modeling to deep learning,” IEEE/ACM Trans. Audio, Speech and Lang. Proc., vol. 29, p. 132–157, jan 2021. [Online]. Available: https://doi.org/10.1109/TASLP.2020.3038524 Y. Ren, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Almost unsupervised text to speech and automatic speech recognition,” in Proceedings of the 36th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97. PMLR, 09–15 Jun 2019, pp. 5410–5419. [Online]. Available: https://proceedings.mlr.press/v97/ren19a.html Z. Zheng, H. Zhou, S. Huang, J. Chen, J. Xu, and L. Li, “Duplex sequenceto-sequence learning for reversible machine translation,” in Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., vol. 34. Curran Associates, Inc., 2021, pp. 21 070–21 084. [Online]. Available: https://proceedings.neurips.cc/paper/2021/file/afecc60f82be41c1b52f6705ec69e0f1-Paper.pdf J. Gu and X. Kong, “Fully non-autoregressive neural machine translation: Tricks of the trade,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Online: Association for Computational Linguistics, Aug. 2021, pp. 120–133. [Online]. Available: https://aclanthology.org/2021.findings-acl.11 S. MacLane, Categories for the Working Mathematician. New York: SpringerVerlag, 1971, graduate Texts in Mathematics, Vol. 5. M. MacKay, P. Vicol, J. Ba, and R. B. Grosse, “Reversible recurrent neural networks,” in Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., vol. 31. Curran Associates, Inc., 2018. [Online]. Available: https://proceedings.neurips.cc/paper/2018/file/4ff6fa96179cdc2838e8d8ce64cd10a7-Paper.pdf Z. Ziegler and A. Rush, “Latent normalizing flows for discrete sequences,” in Proceedings of the 36th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97. PMLR, 09–15 Jun 2019, pp. 7673–7682. [Online]. Available: http://proceedings.mlr.press/v97/ziegler19a.html Y. Lu*, Z. Li*, D. He, Z. Sun, B. Dong, T. Qin, L. Wang, and T. yan Liu, “Understanding and improving transformer from a multi-particle dynamic system point of view.” in ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations, 2019. [Online]. Available: https://openreview.net/forum?id=pxlqJa21C F. Kreuk, Y. Sheena, J. Keshet, and Y. Adi, “Phoneme boundary detection using learnable segmental features,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 8089–8093. H. Poostchi and M. Piccardi, “A multi-constraint structured hinge loss for namedentity recognition,” in Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association. Sydney, Australia: Australasian Language Technology Association, 4–6 Dec. 2019, pp. 41–46. [Online]. Available: https://aclanthology.org/U19-1006 A. Gibiansky, S. Arik, G. Diamos, J. Miller, K. Peng, W. Ping, J. Raiman, and Y. Zhou, “Deep voice 2: Multi-speaker neural text-to-speech,” in Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/c59b469d724f7919b7d35514184fdc0f-Paper.pdf S. Ö. Arık, M. Chrzanowski, A. Coates, G. Diamos, A. Gibiansky, Y. Kang, X. Li, J. Miller, A. Ng, J. Raiman, S. Sengupta, and M. Shoeybi, “Deep voice: Real-time neural text-to-speech,” in Proceedings of the 34th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70. PMLR, 06–11 Aug 2017, pp. 195–204. [Online]. Available: https://proceedings.mlr.press/v70/arik17a.html K. Ito and L. Johnson, “The lj speech dataset,” https://keithito.com/LJ-Speech-Dataset/, 2017. M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi,” in Proc. Interspeech 2017, 2017, pp. 498–502. R. Badlani, A. Łańcucki, K. J. Shih, R. Valle, W. Ping, and B. Catanzaro, “One tts alignment to rule them all,” in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 6092–6096. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83100 | - |
dc.description.abstract | 本論文之主軸在於探討利用可逆之類神經網路層建構雙工語言鏈模型,並藉此讓源自平行資料集的雙向監督訊號充分發揮其功效。目前使用雙向監督訊號的方法,主要分成兩種類型:一般的多任務學習以及循環一致性。兩者雖然都使用到雙向監督訊號,但這些方法都有各自的缺點。為了賦予模型雙工性,並且實踐在由語音合成及語音辨識所組成的雙向語言鏈任務上,我們提出了各種可逆的模組及操作,同時也解決了文字與語音長度不匹配的這項挑戰。
而本論文所提出之模型是第一個能夠同時處理語音合成及語音辨識的雙工模型,也是第一篇將可逆類神經網路運用在語音任務上的文獻。我們將透過實驗分析是否使用雙向監督訊號將對雙工模型的效能造成何種影響。 | zh_TW |
dc.description.abstract | The main point of this paper is to explore how to use reversible neural network layers to construct a duplex speech chain model, and thereby make full use of bidirectional supervision signals from parallel datasets. Current methods using bidirectional supervision signals are mainly divided into two categories: general multi-task learning and cycle consistency. Although both categories use bidirectional supervision signals, these methods have their own shortcomings. In order to make the model duplex and apply on the bidirectional speech chain task consisting of speech synthesis and speech recognition, we propose several reversible modules and operations that also tackle the challenge of mismatching text and speech lengths.
The proposed model is the first duplex sequence-to-sequence model that can handle both speech synthesis and speech recognition problems, and this is also the first research that applies reversible neural networks to tasks related to speech. And we will analyze how the performance of the duplex model is affected by the use of bidirectional supervision signals. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-01-08T17:04:44Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-01-08T17:04:44Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要 ii 英文摘要 iii 一、導論 1 1.1 研究動機 2 1.2 研究方向 4 1.3 章節安排 4 二、背景知識 5 2.1 類神經網路 (Deep Neural Networks) 5 2.1.1 前饋式類神經網路 (Feedforward Neural Network) 6 2.1.2 類神經網路之訓練方式 7 2.1.3 殘差網路 (Residual Network) 9 2.1.4 卷積式類神經網路 (Convolution Neural Network) 10 2.1.5 遞歸式類神經網路 (Recurrent Neural Network) 13 2.1.6 轉換器類神經網路 (Transformer Neural Network) 14 2.1.7 卷積增強轉換器 (Convolution Augmented Transformer) 20 2.2 序列到序列問題 (Sequence to Sequence Problem) 21 2.2.1 自回歸模型 (Autoregressive Model) 22 2.2.2 編碼器解碼器結構 (Encoder-Decoder) 23 2.2.3 非自迴歸模型 (Non-Autoregressive Model) 27 2.3 可逆之類神經架構 (Reversible Neural Architectures) 30 2.3.1 標準化流 (Normalizing Flow) 30 2.3.2 仿射耦合層 (Affine Coupling Layer) 32 2.3.3 激勵正規化 (Activation Normalization) 34 三、基於雙工序列到序列學習之可逆語言鏈 35 3.1 簡介 35 3.1.1 雙工模型 (Duplex Model) 38 3.1.2 現行序列到序列模型對可逆性的限制 40 3.2 方法介紹 42 3.2.1 可逆雙工轉換器 (Reversible Duplex Transformer) 42 3.2.2 長度調節模組 (Length Regulation Module) 47 3.2.3 可逆之可變資訊預測模組 (Reverible Variance Prediction Module) 54 3.3 本章總結 56 四、實驗設計與結果探討 58 4.1 簡介 58 4.2 資料集 58 4.3 實驗評量方法 59 4.3.1 梅爾倒頻譜失真 (Mel Cepstral Distortion, MCD) 59 4.4 實驗設置 60 4.5 實驗結果與討論 60 4.5.1 雙向監督訊號對模型之影響 60 4.6 本章總結 64 五、結論與展望 65 5.1 研究貢獻與討論 65 5.2 未來展望 65 參考文獻 66 | - |
dc.language.iso | zh_TW | - |
dc.title | 分析基於雙工序列到序列模型之語言鏈 | zh_TW |
dc.title | An Analysis of Duplex Sequence-to-Sequence Learning for Speech Chain | en |
dc.title.alternative | An Analysis of Duplex Sequence-to-Sequence Learning for Speech Chain | - |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 曹昱;蔡宗翰 | zh_TW |
dc.contributor.oralexamcommittee | Yu Tsao;Tzong-Han Tsai | en |
dc.subject.keyword | 語言鏈,可逆性,雙工性, | zh_TW |
dc.subject.keyword | Speech chain,Reversibility,Duplexity, | en |
dc.relation.page | 77 | - |
dc.identifier.doi | 10.6342/NTU202210030 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2022-11-08 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 電信工程學研究所 | - |
dc.date.embargo-lift | 2027-11-02 | - |
顯示於系所單位: | 電信工程學研究所 |
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