請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97048完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 范家銘 | zh_TW |
| dc.contributor.advisor | Chia-Ming Fan | en |
| dc.contributor.author | 鮑開立 | zh_TW |
| dc.contributor.author | Kai-Li Bao | en |
| dc.date.accessioned | 2025-02-26T16:12:57Z | - |
| dc.date.available | 2025-02-27 | - |
| dc.date.copyright | 2025-02-26 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-02-05 | - |
| dc.identifier.citation | Achieng, S., Majuto, C. M., Aseka, P., & Astiaya, E. (2019). Replacing humans with machines: Threats and opportunities. East African Journal of Business and Economics, 1(2), 54–68.
Agostinelli,V., Wild, M., Raffel, M., Fuad, K. A. A., & Chen, L. (2023). Simul-LLM: A framework for exploring high-quality simultaneous translation with large language models. arXiv. https://doi.org/10.48550/arXiv.2312.04691 Aliprandi, C., Scudellari, C., Gallucci, I., Piccinini, N., Raffaelli, M., Pozo, A., Álvarez, A., Arzelus, H., Cassaca, R., Luis, T., Neto, J., Mendes, C., Paulo, S., & Viveiros, M. (2014). Automatic live subtitling: State of the art, expectations and current trends. Proceedings of NAB Broadcast Engineering Conference: Papers on Advanced Media Technologies, Las Vegas. Alonso-Bacigalupe, L., & Romero-Fresco, P. (2023). Interlingual live subtitling: the Crossroads between translation, interpreting and accessibility. Universal Access in the Information Society. https://doi.org/10.1007/s10209-023-01032-8 Austermühl, F. (2001). Electronic tools for translators (1st ed.). Routledge. https://doi.org/10.4324/9781315760353 Azti, Irma, Nababan, Mangatur, & Djatmika, Djatmika. (2019). The analysis of the results of acceptability on the translation results in the unedited version and edited version in the novel “After You”. International Journal of Multicultural and Multireligious Understanding, 6(4), 422－432. https://doi.org/10.18415/ijmmu.v6i4.980 Baddeley, A. (1992). Working memory. Science, 255(5044), 556–559. https://doi.org/10.1126/science.1736359 Baldo-de Brébisson, S. (2019). Comparison between automatic and human subtitling: A case study with Game of Thrones. Proceedings of the Second Workshop Human-Informed Translation and Interpreting Technology. Bentivogli, L., Cettolo, M., Gaido, M., Karakanta, A., Martinelli, A., Negri, M., & Turchi, M. (2021). Cascade versus direct speech translation: Do the differences still make a difference? arXiv. arXiv:2106.01045. Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2016). Neural versus phrase-based machine translation quality: A case study. The 2016 Conference on Empirical Methods in Natural Language Processing. Borell, J. (2000). Subtitling or dubbing? An investigation of the effects from reading subtitles on understanding audiovisual material. Cognitive Science, 1, 61–80. Bywood, L., Georgakopoulou, P., & Etchegoyhen, T. (2017). Embracing the threat: Machine translation as a solution for subtitling. Perspectives: Studies in Translatology, 3, 492–508. Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: the Role of brand loyalty. Journal of Marketing, 65(2), 81–93. Chen, C.-J. (2018). Investigating viewer’s reliance on captions based on gaze information [Unpublished master’s thesis]. National Chengchi University. Cheung, A. K. F. (2022). Listeners’ perceptions of the quality of simultaneous interpreting and perceived dependence on simultaneous interpreting. Interpreting, 24(1), 38–58. Chiaro, D. (2012). Audiovisual translation. The Encyclopedia of Applied Linguistics. https://doi.org/10.1002/9781405198431.wbeal0061 Chinomona, R. (2016). Brand communication, brand image and brand trust as antecedents of brand loyalty in Gauteng Province of South Africa. African Journal of Economic and Management Studies, 7(1), 124–139. https://doi.org/10.1108/ajems-03-2013-0031 Choung, H., David, P., & Ross, A. (2022). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human-Computer Interaction, pp. 1–13. https://doi.org/10.1080/10447318.2022.2050543 Christodoulides, G. & Langlet, C. (2014). Prosodic correlates of perceived quality and fluency in simultaneous interpreting. In Campbell, N., Gibbon, D., & Hirst. D. (Eds.), Proceedings of the 7th Speech Prosody Conference (pp. 1002–1006). Science Foundation Ireland. Christoffels, I. K., & de Groot, A. M. B. (2005). Simultaneous interpreting: A cognitive perspective. In Kroll, J. F. & de Groot, A. M. B. (Eds.), Handbook of bilingualism: psycholinguistic approaches (pp. 454–479). Oxford University Press. Díaz-Cintas, J., Orero, P., & Remael, A. (2007). Media for all: A global challenge. In Díaz-Cintas, J., Orero, P., & Remael, A. (Eds.) Media for all: Subtitling for the Deaf, audio description, and sign language (pp. 11–20). Brill Academic Hub. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral dissertation]. Massachusetts Institute of Technology. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 Dayter, D. (2020). Variation in non-fluencies in a corpus of simultaneous interpreting vs. non-interpreted English. Perspectives, 29(4), 489–506. https://doi.org/10.1080/0907676X.2020.1718170 Delgado-Ballester, E., Munuera-Alemán, J.-L., & Yagüe-Guillén, M. J. (2003). Development and validation of a trust scale. International Journal of Market Research, 46(1), 35–56. Den Boer, C. M. (2001). Live interlingual subtitling. In Gambier Y. & Gottlieb H. (Eds.), (Multi)media translation: Concepts, practices, and research (pp 167–172). John Benjamins. https://doi.org/10.1075/btl.34.20boe De Korte, T. (2006). Live inter-lingual subtitling in the Netherlands: Historical background and current practice. InTRAlinea Special issue: Respeaking. Desblache, L. (2021). Music to my ears, but words to my eyes? Text, opera and their audiences. Linguistica Antverpiensia, New Series—Themes in Translation Studies, 6. https://doi.org/10.52034/lanstts.v6i.185 Deutsch, M. (1973). The resolution of conflict, constructive and destructive processes. Yale University Press. https://doi.org/10.1177/000276427301700206 Djatmiko, T., & Pradana, R. (2016). Brand image and product price; its impact for Samsung smartphone purchasing decision. Procedia—Social and Behavioral Sciences, 219, 221–227. https://doi.org/10.1016/j.sbspro.2016.05.009 Dumortier, J., Evans, K. S., Grebitus, C., & Martin, P. A. (2017). The influence of trust and attitudes on the purchase frequency of organic produce. Journal of International Food & Agribusiness Marketing, 29(1), 46–69. https://doi.org/10.1080/08974438.2016.1266565 d’Ydewalle, G., Praet, C., Verfaillie, K., & Rensbergen, J. V. (1991). Watching subtitled television: Automatic reading behavior. Communication Research, 18(5), 650–666. https://doi.org/10.1177/009365091018005005 d’Ydewalle, G., & De Bruycker, W. (2007). Eye movements of children and adults while reading television subtitles. European Psychologist, 12(3), 196–205. https://doi.org/10.1027/1016-9040.12.3.196 Dzindolet, M. T., Pierce, L. G., Beck, H. P., & Dawe, L., A. (2002). The perceived utility of human and automated aids in a visual detection task. Human Factors, 44(1), 79–94. https://doi.org/10.1518/0018720024494856 Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv. https://doi.org/10.48550/arXiv.2303.10130 European Commission (2012). Europeans and their languages (Special Eurobarometer 386). Fantinuoli, C., & Prandi, B. (2021). Towards the evaluation of automatic simultaneous speech translation from a communicative perspective. Proceedings of the 18th International Conference on Spoken Language Translation (pp 245–254). Association for Computational Linguistics. Flanagan, M. (2009). Recycling texts: Human evaluation of example-based machine translation subtitles for DVD [Unpublished doctoral dissertation]. Dublin City University. Gebru, B., Zeleke, L., Blankson, D., Nabil, M., Nateghi, S., Homaifar, A., & Tunstel, E. (2022). A review on human-machine trust evaluation: Human-centric and machine-centric perspectives. IEEE Transactions on Human-Machine Systems, 52(5), 952–962. https://doi.org/10.1109/thms.2022.3144956 Gieshoff, A. C., & Albl-Mikasa, M. (2022). “Interpreting accuracy revisited: a refined approach to interpreting performance analysis.” Perspectives, 32(2), 210–228. https://doi.org/10.1080/0907676X.2022.2088296 Hagström, H., & Pedersen, J. (2022). Subtitles in the 2020s: The influence of machine translation. Journal of Audiovisual Translation, 5(1), 207–225. https://doi.org/10.47476/jat.v5i1.2022 Hendy, A., Abdelrehim, M., Sharaf, A., Raunak, V., Gabr, M., Matsushita, H., Kim, Y. J., Afify, M., & Awadalla, H. H. (2023). How good are GPT models at machine translation? A comprehensive evaluation. arXiv preprint. https://doi.org/10.48550/arXiv.2302.09210 Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105–120. https://doi.org/10.1016/j.techfore.2015.12.014 Hirvonen, M. & Kinnunen, T. (2020). Accessibility and linguistic rights. In Koskinen, K. & Pokorn, N. K. (Eds.), The Routledge Handbook of Translation and Ethics. Routledge. Hoff, K. A. & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57(3), 407–434. https://doi.org/10.1177/0018720814547570 Horváth, I. (2021). Speech translation vs. Interpreting. Language Studies and Modern Humanities, 3(2), 174–187. https://doi.org/10.33910/2686-830x-2021-3-2-174-187 Hu, K., O’Brien, S., & Kenny, D. (2020). A reception study of machine translated subtitles for MOOCs, Perspectives, 28(4), 521–538. https://doi.org/10.1080/0907676X.2019.1595069 Huang, L. L. (2006). A Study on the translation of Chinese subtitles. Based on the film of “Goodbye, Lenin!” [Unpublished master’s thesis]. Fu Jen Catholic University. International Telecommunication Union (2018). ITU-T Recommendations F.791: Accessibility Terms and Definitions. https://www.itu.int/itut/recommendations/rec.aspx?rec=13661 Ivarsson, J. (2002). Subtitling through the ages: A technical history of subtitles in Europe. Language International, 14(2), 6–10. Juang, B. H. & Rabiner, L. R. (2004). Automatic Speech Recognition – A Brief History of the Technology Development. Elsevier. Keding, C., & Meissner, P. (2021). Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions. Technological Forecasting and Social Change, 171. https://doi.org/10.1016/j.techfore.2021.120970 Kilborn, R. (1993). “Speak my language”: Current attitudes to television subtitling and dubbing. Media, Culture, and Society, 15, 641–660. Kim, D. H. (2023). A study on the job replacement impact of ChatGPT and education method. European Journal of Science, Innovation and Technology, 3(4), 105–122. Koolstra, C. M., Peeters, A. L., & Spinhof, H. (2002). The pros and cons of dubbing and subtitling. European Journal of Communication, 17(3), 325–354. Kurz, I. (1993). Conference Interpretation: Expectations of Different User Groups. The Interpreters’ Newsletter, 5. Kurz, I. (2001). Conference interpreting: Quality in the ears of the user. Meta, 46(2), 394–409. https://doi.org/10.7202/003364ar Lee, J., & Moray, N. (1992). Trust, control strategies and allocation of function in human-machine systems. Ergonomics, 35(10), 1243–1270. https://doi.org/10.1080/00140139208967392 Lee, J. D., & Moray, N. (1994). Trust, self-confidence, and operators’ adaptation to automation. International Journal of Human-Computer Studies, 40, 153–184. Lewandowsky, S., Mundy, M., & Tan, G. P. A. (2000). The dynamics of trust: Comparing humans to automation. Journal of Experimental Psychology: Applied, 6(2), 104–123. https://doi.org/10.1037//K.1037//1076-898X.6.2.104 Lin,Y., Lv, Q., & Liang, J. (2018). Predicting fluency with language proficiency, working memory, and directionality in simultaneous interpreting. Front. Psychol. 9:1543. https://doi.org/10.3389/fpsyg.2018.01543 Lopez, A. (2008). Statistical machine translation. ACM Computing Surveys, 40(3), 1–49. https://doi.org/10.1145/1380584.1380586 Lu, X., Li, S., Fujimoto, M. (2020). Automatic speech recognition. In Kidawara, Y., Sumita, E., & Kawai, H. (Eds.), Speech-to-Speech Translation (pp. 21–38). Springer. https://doi.org/10.1007/978-981-15-0595-9_2 Mateo, M. (2012). Music and translation. In Gambier, Y. & van Doorslaer, L. (Eds.), Handbook of Translation Studies (3rd ed.; pp. 115–121). John Benjamins Publishing Company. Mayer, R. E. (2014). Cognitive theory of multimedia learning. In Mayer, R. E. (Ed.), The Cambridge Handbook of Multimedia Learning (pp. 43–71). Cambridge University Press. McDonald, S. M. (2011). Perception: A concept analysis. International Journal of Nursing Terminologies and Classifications. https://doi.org/10.1111/j.1744-618X.2011.01198.x Mera, M. (1999). Read my lips: Re-evaluating subtitling and dubbing in Europe. Links & Letters, 6. Miao, C. (2016). Differences between human and machine translations: A quantitative analysis based on Coh-Metrix 3.0 and CLAWS Tagger [Unpublished master’s thesis]. National Taiwan University. Miao, M., Meng, F., Liu, Y., Zhou, X.-H., & Zhou J. (2021). Prevent the language model from being overconfident in neural machine translation. arXiv. https://doi.org/10.48550/arXiv.2105.11098 Mishra, N., Shrawankar, U., & Thakare, V. M. (2013). Automatic speech recognition using template model for man-machine interface. arXiv. https://doi.org/10.48550/arXiv.1305.2959 Moniz, H., Trancoso, I., & Mata, A. I. (2009). Classification of disfluent phenomena as fluent communicative devices in specific prosodic contexts. Conference Proceedings of Interspeech 2009. 10.21437/Interspeech.2009-518 Moorkens, J. (2018). What to expect from neural machine translation: a Practical in-class translation evaluation exercise. The Interpreter and Translator Trainer, 12(4), 375–387. https://doi.org/10.1080/1750399x.2018.1501639 Moreau, J. T., Baillet, S., & Dudley, R. W. R. (2020). Biased intelligence: On the subjectivity of digital objectivity. BMJ Health & Care Informatics. https://doi.org/10.1136/bmjhci-2020-100146 Muir, B. M. (1987). Trust between humans and machines. International Journal of Man-Machine Studies, 27, 527–539. Ng, B. C. (1992). End users’ subjective reaction to the performance of student interpreters [Special issue]. The Interpreters’ Newsletter, 1, 35–41. Nuttavuthisit, K., & Thøgersen, J. (2015). The importance of consumer trust for the emergence of a market for green products: The case of organic food. Journal of Business Ethics, 140(2), 323–337. https://doi.org/10.1007/s10551-015-2690-5 Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63, 33–44. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, K., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. arXiv. https://doi.org/10.48550/arXiv.2203.02155 Perego, E., Del Missier, F., & Stragà, M. (2018). Dubbing vs. subtitling: Complexity matters. Target, 30(1), 137–157. https://doi.org/10.1075/target.16083.per Petite, C. (2005). Evidence of repair mechanisms in simultaneous interpreting: A corpus-based analysis. Interpreting, 7(1), 27–49. Pielmeier, H., Lommel, A., & Toon, A. (2024). Perceptions on Automated Interpreting: Results of a Large-Scale Study of End-Users, Requestors, and Providers of Interpreting Services and Technology. CSA Research. Pöchhacker, F. (2016). Introducing Interpreting Studies (2nd ed.). Routledge. https://doi.org/10.4324/9781315649573 Pöchhacker, F. (2018). Moving boundaries in interpreting. In Van Dam, H., Brøgger, M. N., & Zethsen, K. K. (Eds.) Moving Boundaries in Translation Studies (pp. 45–63). Routledge. Pym, A. (2020, July 29). When trust matters more than translation. The University of Melbourne. https://pursuit.unimelb.edu.au/articles/when-trust-matters-more-than-translation Rabiee, F. (2004). Focus-group interview and data analysis. Proceedings of the Nutrition Society, 63(4), 655–660. https://doi.org/10.1079/PNS2004399 Rempel, J. K., Holmes, J. G., & Zanna, M. P. (1985). Trust in close relationships. Journal of Personality and Social Psychology, 49, 95–112. http://dx.doi.org/10.1037/0022-3514.49.1.95 Riniolo, T. C., & Capuana, L. J. (2020). Directly comparing subtitling and dubbing using Netflix: Examining enjoyment issues in the natural setting. Current Psychology, 41(7), 4252–4258. https://doi.org/10.1007/s12144-020-00948-1 Ritchie, J., & Spencer, L. (2002). Qualitative data analysis for applied policy research. In Huberman, A. M., & Miles, M. B. (Eds.), The Qualitative Researcher’s Companion (pp. 305–329). SAGE Publications. https://doi.org/10.4135/9781412986274 Romero-Fresco, P., & Pöchhacker, F. (2017). Quality assessment in interlingual live subtitling: The NTR Model. Linguistica Antverpiensia, New Series: Themes in Translation Studies, 16, 149–167. Romero-Fresco, P. & Alonso-Bacigalupe, L. (2022). An empirical analysis on the efficiency of five interlingual live subtitling workflows. XLinguae, 15, 3–16. https://doi.org/10.18355/XL.2022.15.02.01 Rotter, J. B. (1980). Interpersonal trust, trustworthiness, and gullibility. American Psychologist, 35, 1–7. https://doi.org/10.1037/0003-066X.35.1.1 Scanzoni, J. (1979). Sex-role influences on married women’s status attainments. Journal of Marriage and the Family, 41(4), 793–800. https://doi.org/10.2307/351479 Seleskovitch, D. (1986). Comment: Who should assess an interpreter’s performance? Multilingua, 5(4), 236–236. https://doi.org/10.1515/mult.1986.5.4.236 Shlesinger, M. (1997). Quality in simultaneous interpreting. In Gambier, Y., Gile, D., & Taylor, C. (Eds.) Conference Interpreting: Currrent Trends in Research (pp. 123–131). John Benjamins. Simpson, T. W. (2012). What is trust? Pacific Philosophical Quarterly, 93(4), 550–569. https://doi.org/10.1111/j.1468-0114.2012.01438.x Sloculn, J. (1985). A survey of machine translation: Its history, current status, and future prospects. Computational Linguistics, 11(1). Sperber, M. & Paulik, M. (2020). Speech translation and the end-to-end promise: Taking stock of where we are. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 7409–7421. Sreelekha, S., Bhattacharyya, P., & Malathi, D. (2018). Statistical vs. rule-based machine translation: A comparative study on Indian languages. In Dash, S., Das, S., & Panigrahi, B. (Eds.), International Conference on Intelligent Computing and Applications (Vol. 632, pp. 663–675). Springer. https://doi.org/10.1007/978-981-10-5520-1_59 Szarkowska, A., & Gerber-Morón, O. (2018). Two or three lines: a mixed-methods study on subtitle processing and preferences. Perspectives, 27(1), 144–164. https://doi.org/10.1080/0907676X.2018.1520267 Szarkowska A. & Gerber-Morón O. (2018). Viewers can keep up with fast subtitles: Evidence from eye movements. PLoS ONE, 13(6). https://doi.org/10.1371/journal.pone.0199331 Taiwan Ministry of Education (2018). Curriculum Guidelines of 12-Year Basic Education for Elementary School, Junior High and General Senior High Schools: Subject of English in the Domain of Language. https://cirn.moe.edu.tw/WebContent/index.aspx?sid=11&mid=13543 Wajcman, J. (2017). Automation: is it really different this time? The British Journal of Sociology, 68(1), 1–142. Wang, H., Wu, H., He, Z., Huang, L., & Church, K. W. (2022). Progress in machine translation. Engineering, 18, 143–153. https://doi.org/10.1016/j.eng.2021.03.023 Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A., Johnson, M., Liu, X., Kaiser, Ł., Gouws, S., Kato, Y., Kudo, T., Kazawa, H., Stevens, K., Kurian, G., Patil, N., Wang, W., Young, C., Smith, J., Riesa, J., Rudnick, A., Vinyals, O., Corrado, G., Hughes, M., & Dean, J. (2017). Google’s neural machine translation system: Bridging the gap between human and machine translation. Computing Research Repository. https://doi.org/10.48550/arXiv.1609.08144 Zong, Z. (2018). Research on the relations between machine translation and human translation. Journal of Physics: Conference Series, 1087(6). https://doi.org/10.1088/1742-6596/1087/6/062046 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97048 | - |
| dc.description.abstract | 人工智慧科技的進步帶動了即時字幕翻譯的發展，Wordly AI和Zoom等平台已能提供即時字幕翻譯服務，使口譯員開始擔心職業會受到威脅。儘管目前已有不少研究比較機器與人類的翻譯能力，卻少有研究探討觀眾對聆聽人類口譯與閱讀機器即時字幕翻譯的觀感差異。
本研究招募29位臺灣聽眾並調查其對同步口譯與機器即時字幕翻譯之觀感與偏好，為往後更大規模的相關研究提供參考。研究參與者首先觀看一段有英文及俄文演講的影片，兩演講均提供同步口譯或機器即時字幕翻譯服務，影片及翻譯服務的安排並採用對抗平衡設計抵銷下列三項變數的影響：翻譯模式（聆聽同步口譯或閱讀機器即時字幕翻譯）、翻譯的來源語（英文或俄文）、演講的順序（英文先或俄文先）。影片結束後進行焦點團體訪談，探討參與者對同步口譯及機器即時字幕翻譯之觀感與偏好，以及背後原因。 結果顯示，參與者整體給予口譯員的表現高度評價，並偏好使用其服務，該觀點主要受到參與者對兩翻譯模式翻譯品質的主觀看法影響，參與者對來源語及模式的熟悉度則為形塑其觀感與偏好的次要影響因素。令人意外的是，信任感在本研究中並未造成顯著影響。理想即時翻譯模式方面，即使許多參與者表示同步口譯已是令人滿意的模式，但有更多參與者希望同步口譯內容能以字幕方式呈現，顯示參與者仍習慣閱讀字幕，且即時翻譯服務未來可朝向人機器合作發展。 本研究也發現，部分參與者無法容忍同步口譯當中出現短暫的時間差及少數不自然停頓和重複語句，並認為機器即時字幕翻譯軟體在精確度、客觀度、精力等方面均更勝口譯員，顯示其對口譯員抱有不切實際的期待，並對機器即時字幕翻譯軟體的能力抱有過度信心，背後原因可能是參與者使用同步口譯的經驗不足，或是對現代科技的知識有限。 綜上所述，本研究提供臺灣研究參與者對同步口譯與機器即時字幕翻譯觀感與偏好的概覽，並整理出兩翻譯模式在參與者眼中的優缺點及理想模式，為同步口譯及機器即時字幕翻譯的未來發展提供參考依據。結果顯示，口譯員和即時翻譯軟體不是敵人，兩者應攜手合作，為即時翻譯使用者提供最理想的解決方案。 | zh_TW |
| dc.description.abstract | Advancements in AI technology have enabled the development of live translated subtitling services, such as Wordly AI and Zoom’s live subtitles, sparking concerns among spoken language interpreters about the potential threat to their profession. While there has been substantial research comparing the translation abilities of machines and humans, little attention has been given to the perceptions of audience members when comparing listening to human interpreters and reading machine-generated live subtitles.
This study reports the findings of the pilot phase of a larger investigation into the preferences and perceptions of 29 Taiwanese audience members regarding simultaneous interpreting (SI) and machine-generated live subtitles (MGLS). Participants in the study watched a video featuring an English and a Russian speech, which were either simultaneously interpreted into Chinese by a human interpreter or translated into Chinese subtitles using MGLS software. The participants were counterbalanced across three variables: the mode of translation provided (listening to SI or reading MGLS), the source language interpreted or subtitled (English or Russian), and the sequence of the two speeches (English or Russian first). Focus group interviews were then conducted to explore participants’ perceptions and preferences of MGLS and SI and the underlying reasons. The results indicate that participants generally rated interpreters’ performance highly and preferred their services. Their responses were mostly influenced by their perceived quality of the modes. While source language familiarity and mode familiarity also played a role in shaping their perceptions and preferences, trust was surprisingly not a significant factor. Regarding participants’ ideal live translation modes, while many thought SI was already satisfactory, more hoped that SI could be transcribed and shown as subtitles, indicating participants’ habit of reading subtitles and suggesting human-machine cooperation as a possible future development of live translation services. This study also discovered that some participants tended to have impractical expectations for interpreters and were overly optimistic about MGLS’s abilities, as they could not tolerate a few seconds of time lag and a few unnatural pauses and sentence repetitions in SI, and they thought MGLS was all the better in terms of accuracy, objectivity, and stamina, which may be due to their inexperience in using SI and limited knowledge about modern technology. In conclusion, this study provides an overall look into Taiwanese participants’ perceptions and preferences of SI and MGLS, highlighting the current perceived strengths and downsides of each mode, and indicating the participants’ ideal modes to serve as a reference for future SI and MGLS development. The results suggest that interpreters and live translation software are not rivals. Instead, they should work together to generate an optimal solution for live translation users. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-26T16:12:57Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-02-26T16:12:57Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘要 i
Abstract ii List of Figures vi List of Tables vii Chapter 1 Introduction 1 Chapter 2 Literature Review 4 2.1 Machine Speech-to-Text Translation 4 2.2 Machine-Generated Live Subtitles 6 2.3 The Audience’s Perceptions of MT and SI 9 2.3.1 User Satisfaction 11 2.3.2 Trust 11 2.3.3 Source Language Familiarity 13 2.3.4 Reading Subtitles and Listening to Interpretation 14 Chapter 3 Methods 18 3.1 Participants 18 3.2 Materials 18 3.2.1 Pre-session Questionnaire 18 3.2.2 Stimuli 19 3.2.3 Comprehension Test 24 3.2.4 Interview Questions 24 3.3 Procedure 25 3.4 Data Collection and Analysis 28 Chapter 4 Results 31 4.1 Pre-session Questionnaire 31 4.2 Quantitative Results 32 4.2.1 Comprehension Tests 32 4.2.2 Assessments of the SI and the MGLS 33 4.3 Interview Results 34 4.3.1 Perceptions of SI and MGLS 35 4.3.3 Preferences 49 Chapter 5 Discussion 56 5.1 Variations in the Participants’ Responses 57 5.1.1 Repetition 58 5.1.2 Subtitle Display Speed, Sets and Lines 60 5.1.3 Pauses and Lags 63 5.1.4 Soundtrack Distractions 65 5.2 Misconceptions of SI and MGLS 66 5.2.1 Confusions 67 5.2.2 Impractical Expectations for SI 68 5.2.3 Overconfidence in Machines 69 5.3 Determinants of the Participants’ Perceptions and Preferences 71 5.3.1 Determinants of Perceptions 71 5.3.2 Determinants of Preferences 72 5.4 Accessibility of SI and MGLS 74 Chapter 6 Conclusion 78 6.1 Summary of the Study 78 6.2 Limitations and Recommendations for Future Studies 79 References 83 Appendix A 96 Appendix B 97 Appendix C 101 | - |
| dc.language.iso | en | - |
| dc.title | 同步口譯與機器即時字幕翻譯之觀感與偏好：以臺灣觀眾為例 | zh_TW |
| dc.title | Perceptions and Preferences of Simultaneous Interpreting Versus Machine-Generated Live Subtitles: A Case Study on Audiences in Taiwan | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 吳敏嘉;張其帆 | zh_TW |
| dc.contributor.oralexamcommittee | Min-Chia Wu;Kay-Fan Cheung | en |
| dc.subject.keyword | 同步口譯,機器即時字幕翻譯,觀眾的觀感,偏好,神經機器翻譯, | zh_TW |
| dc.subject.keyword | simultaneous interpreting,machine-generated live subtitles,audience perceptions,preferences,neural-based machine translation, | en |
| dc.relation.page | 101 | - |
| dc.identifier.doi | 10.6342/NTU202500430 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-02-06 | - |
| dc.contributor.author-college | 文學院 | - |
| dc.contributor.author-dept | 翻譯碩士學位學程 | - |
| dc.date.embargo-lift | 2025-02-27 | - |
| 顯示於系所單位： | 翻譯碩士學位學程 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-1.pdf | 1.29 MB | Adobe PDF | 檢視/開啟 |
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。