Machine translation of English speech: Comparison of multiple algorithms
In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the en...
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| Veröffentlicht in: | Journal of intelligent systems Jg. 31; H. 1; S. 159 - 167 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Berlin
De Gruyter
01.01.2022
Walter de Gruyter GmbH |
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| ISSN: | 2191-026X, 0334-1860, 2191-026X |
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| Abstract | In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length. |
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| AbstractList | In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length. |
| Author | Qin, Yonghong Wu, Yijun |
| Author_xml | – sequence: 1 givenname: Yijun surname: Wu fullname: Wu, Yijun email: y6w8yi@163.com organization: Department of Foreign Languages, Xi’an Jiaotong University City College, Xi’an, Shaanxi 710018, China – sequence: 2 givenname: Yonghong surname: Qin fullname: Qin, Yonghong organization: School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China |
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| Cites_doi | 10.18653/v1/P16-1208 10.1109/ACCESS.2020.3039539 10.1051/e3sconf/202127312140 10.1093/jamia/ocz110 10.1088/1742-6596/1744/3/032019 10.21071/hikma.v19i2.12516 10.1007/s10590-019-09227-8 10.1017/S1351324919000469 10.1080/0907676X.2017.1291695 10.1162/tacl_a_00067 10.1007/s41870-019-00340-8 10.1088/1755-1315/687/1/012205 10.1007/s10590-021-09262-4 |
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| References | Li, S (j_jisys-2022-0005_ref_015) 2021; 1744 Ashengo, YA; Aga, RT; Abebe, SL (j_jisys-2022-0005_ref_004) 2021; 35 Herbig, N; Pal, S; Vela, M; Krüger, A; van Genabith, J (j_jisys-2022-0005_ref_003) 2019; 33 Plaza-Lara, C (j_jisys-2022-0005_ref_013) 2020; 19 Chatzikoumi, E (j_jisys-2022-0005_ref_007) 2019; 26 Niyazbek, M; Talp, K; Sun, J (j_jisys-2022-0005_ref_009) 2021; 687 Bywood, L; Georgakopoulou, P; Etchegoyhen, T (j_jisys-2022-0005_ref_010) 2017; 25 Ren, Q; Su, Y; Wu, N (j_jisys-2022-0005_ref_002) 2020; 18 Lee, J; Cho, K; Hofmann, T (j_jisys-2022-0005_ref_005) 2017; 5 Soto, X; Perez-de-Viñaspre, O; Labaka, G; Oronoz, M (j_jisys-2022-0005_ref_008) 2019; 26 Bayatli, S; Kurnaz, S; Ali, A; Washington, JN; Tyers, FM (j_jisys-2022-0005_ref_001) 2020; 36 Gritsay, I; Vodyanitskaya, L (j_jisys-2022-0005_ref_014) 2021; 273 Xiao, Q; Chang, X; Zhang, X; Liu, X (j_jisys-2022-0005_ref_011) 2020; 8 2022120618435633074_j_jisys-2022-0005_ref_009 2022120618435633074_j_jisys-2022-0005_ref_007 2022120618435633074_j_jisys-2022-0005_ref_008 2022120618435633074_j_jisys-2022-0005_ref_005 2022120618435633074_j_jisys-2022-0005_ref_006 2022120618435633074_j_jisys-2022-0005_ref_003 2022120618435633074_j_jisys-2022-0005_ref_014 2022120618435633074_j_jisys-2022-0005_ref_004 2022120618435633074_j_jisys-2022-0005_ref_015 2022120618435633074_j_jisys-2022-0005_ref_001 2022120618435633074_j_jisys-2022-0005_ref_012 2022120618435633074_j_jisys-2022-0005_ref_002 2022120618435633074_j_jisys-2022-0005_ref_013 2022120618435633074_j_jisys-2022-0005_ref_010 2022120618435633074_j_jisys-2022-0005_ref_011 |
| References_xml | – volume: 273 start-page: 12140 year: 2021 ident: j_jisys-2022-0005_ref_014 article-title: Pedagogical technologies of machine translation skills forming on the example of bachelor students specializing in mechatronics and robotics at Don State Technical University publication-title: E3S Web Conf – volume: 35 start-page: 19 year: 2021 end-page: 36 ident: j_jisys-2022-0005_ref_004 article-title: Context based machine translation with recurrent neural network for English–Amharic translation publication-title: Mach Transl – volume: 25 start-page: 1 issue: 3 year: 2017 end-page: 17 ident: j_jisys-2022-0005_ref_010 article-title: Embracing the threat: machine translation as a solution for subtitling publication-title: Persp Stud Transl – volume: 5 start-page: 365 year: 2017 end-page: 78 ident: j_jisys-2022-0005_ref_005 article-title: Fully character-level neural machine translation without explicit segmentation publication-title: Trans Assoc Comput Linguist – volume: 26 start-page: 1478 issue: 12 year: 2019 end-page: 87 ident: j_jisys-2022-0005_ref_008 article-title: Neural machine translation of clinical texts between long distance languages publication-title: J Am Med Inf Assoc – volume: 18 start-page: 46 issue: 1 year: 2020 end-page: 59 ident: j_jisys-2022-0005_ref_002 article-title: Research on Mongolian-Chinese machine translation based on the end-to-end neural network publication-title: Int J Wavel Multi – volume: 19 start-page: 163 year: 2020 end-page: 82 ident: j_jisys-2022-0005_ref_013 article-title: How does machine translation and post-editing affect project management? An interdisciplinary approach publication-title: Hikma – volume: 8 start-page: 216718 year: 2020 end-page: 28 ident: j_jisys-2022-0005_ref_011 article-title: Multi-information spatial-temporal LSTM fusion continuous sign language neural machine translation publication-title: IEEE Access – volume: 36 start-page: 309 issue: 2 year: 2020 end-page: 22 ident: j_jisys-2022-0005_ref_001 article-title: Unsupervised weighting of transfer rules in rule-based machine translation using maximum-entropy approach publication-title: J Inf Sci Eng – volume: 1744 start-page: 032019 year: 2021 ident: j_jisys-2022-0005_ref_015 article-title: Research on the external communication of Chinese excellent traditional culture from the perspective of machine translation publication-title: J Phys Conf Ser – volume: 33 start-page: 91 issue: 1–2 year: 2019 end-page: 115 ident: j_jisys-2022-0005_ref_003 article-title: Multi-modal indicators for estimating perceived cognitive load in post-editing of machine translation publication-title: Mach Transl – volume: 687 start-page: 012205 issue: 1 year: 2021 ident: j_jisys-2022-0005_ref_009 article-title: The development and construction of bilingual machine translation auxiliary tool between Chinese and Kazakh languages publication-title: IOP Conf Ser Earth Environ Sci – volume: 26 start-page: 1 issue: 2 year: 2019 end-page: 25 ident: j_jisys-2022-0005_ref_007 article-title: How to evaluate machine translation: a review of automated and human metrics publication-title: Nat Lang Eng – ident: 2022120618435633074_j_jisys-2022-0005_ref_012 doi: 10.18653/v1/P16-1208 – ident: 2022120618435633074_j_jisys-2022-0005_ref_011 doi: 10.1109/ACCESS.2020.3039539 – ident: 2022120618435633074_j_jisys-2022-0005_ref_014 doi: 10.1051/e3sconf/202127312140 – ident: 2022120618435633074_j_jisys-2022-0005_ref_008 doi: 10.1093/jamia/ocz110 – ident: 2022120618435633074_j_jisys-2022-0005_ref_015 doi: 10.1088/1742-6596/1744/3/032019 – ident: 2022120618435633074_j_jisys-2022-0005_ref_013 doi: 10.21071/hikma.v19i2.12516 – ident: 2022120618435633074_j_jisys-2022-0005_ref_003 doi: 10.1007/s10590-019-09227-8 – ident: 2022120618435633074_j_jisys-2022-0005_ref_007 doi: 10.1017/S1351324919000469 – ident: 2022120618435633074_j_jisys-2022-0005_ref_010 doi: 10.1080/0907676X.2017.1291695 – ident: 2022120618435633074_j_jisys-2022-0005_ref_005 doi: 10.1162/tacl_a_00067 – ident: 2022120618435633074_j_jisys-2022-0005_ref_006 doi: 10.1007/s41870-019-00340-8 – ident: 2022120618435633074_j_jisys-2022-0005_ref_002 – ident: 2022120618435633074_j_jisys-2022-0005_ref_001 – ident: 2022120618435633074_j_jisys-2022-0005_ref_009 doi: 10.1088/1755-1315/687/1/012205 – ident: 2022120618435633074_j_jisys-2022-0005_ref_004 doi: 10.1007/s10590-021-09262-4 |
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| SubjectTerms | Algorithms Back propagation networks Coders Decoding English Errors long short-term memory Machine translation Neural networks recurrent neural network Recurrent neural networks Speech Speech recognition Translations |
| Title | Machine translation of English speech: Comparison of multiple algorithms |
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