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Design of Oral English Intelligent Evaluation System Based on DTW Algorithm

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Abstract

The accuracy of the existing spoken English intelligent evaluation system is not high, and the spoken English evaluation effect is poor. To improve the accuracy and speed of spoken English evaluation, this paper puts forward the design and research of oral English Intelligent Evaluation System based on DTW algorithm. The DTW algorithm is applied to recognize spoken English speech, and a new spoken English intelligent evaluation system is designed. The hardware units are DSP chip selection unit, spoken English audio acquisition unit and its external memory unit; the software modules are spoken English speech preprocessing module, spoken English speech recognition module and spoken English intelligent evaluation module. Through the design of hardware unit and software module, the operation of spoken English intelligent evaluation system is realized. The experimental results show that the oral evaluation accuracy of this system is 65.63% -76.58%, and the response time is 8.23 ms − 13.57 ms, with high accuracy, high evaluation efficiency and improving the effect of spoken English intelligent evaluation.

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References

  1. Luo GF, Zhao FF (2020) Design of an automatic scoring system for oral English test based on sequence matching. Automation & Instrumentation 6:87–90

    Google Scholar 

  2. Liu X (2018) Research on evaluation system of open oral English teaching for business English majors in secondary vocational schools. J Educ Inst Jilin Province

  3. Cheng WU (2020) Intelligent unmanned systems: important achievements and applications of new generation artificial intelligence. Front Inf Technol Electron Eng 21(5):649–651

    Article  Google Scholar 

  4. Lin L, Liu J, Zhang X et al (2021) Automatic translation of spoken English based on improved machine learning algorithm. J Intell Fuzzy Syst 40(2):2385–2395

    Article  Google Scholar 

  5. Zhang H (2020) Research on spoken English analysis model based on transfer learning and machine learning algorithms. J Intell Fuzzy Syst 38(99):1–11

    Google Scholar 

  6. Mehra A, Shukla A, Kumawat M et al (2010) Intelligent system for speaker identification using lip features with PCA and ICA. Comput Sci 56(6):98–105

    Google Scholar 

  7. Mohmand MI, Bhaumik A, Kumam P et al (2020) The geometrical based lip-reading techniques of multi-dimensional dynamic time warping MDTW and hidden Markov models HMMs in the audio visual speech recognition. Intl J Adv Trends Comput Sci Eng 9(1):496–504

    Article  Google Scholar 

  8. Hector L, Estela et al (2012) Automatic system for identifying and categorizing temporal relations in natural language. Int J Intell Syst 27(7):680–703

    Article  Google Scholar 

  9. Shafiq HM, Tahir B, Mehmood MA (2020) Towards building a Urdu language Corpus using common crawl. J Intell Fuzzy Syst 39(1):1–11

    Google Scholar 

  10. Honma H, Nishimura K, Tamori Y et al (2019) Algorithm for the vertex connectivity problem on circular trapezoid graphs. J Appl Math Phys 7(11):2595–2602

    Article  Google Scholar 

  11. Naser MZ, Seitllari A (2020) Concrete under fire: an assessment through intelligent pattern recognition. Eng Comput 36(4):1915–1928

    Article  Google Scholar 

  12. Oyinloye CA, Fatimayin F, Osikomaiya MO et al (2020) Phonological perversion as detriment to effective use of spoken English among secondary school English teachers in non-native context. Front Educ Technol 3(3):24

    Article  Google Scholar 

  13. Lastres-López C (2020) Subordination and insubordination in contemporary spoken English if-clauses as a case in point. English Today 36(2):48–52

    Article  Google Scholar 

  14. Silva DD, Sierla S, Alahakoon D et al (2020) Toward intelligent industrial informatics: a review of current developments and future directions of artificial intelligence in industrial applications. IEEE Ind Electron Mag 14(2):57–72

    Article  Google Scholar 

  15. Khan IU (2020) Exploring the role of dialogic teaching in improving learners’ spoken English at intermediate level in district Bannu. Sir Syed J Educ Social Res (SJESR) 3(3):90–95

    Article  Google Scholar 

  16. Sderqvist EB (2020) Evidentiality in gendered styles in spoken English. ICAME Journal 44(1):5–35

    Article  Google Scholar 

  17. Castello E, Gesuato S (2019) Holding up one’s end of the conversation in spoken English: lexical backchannels in L2 examination discourse. Intl J Learner Corpus Res 5(2):231–252

    Article  Google Scholar 

  18. Osikomaiya O, Oyinloye C (2019) The effects of voiceless glottal fricative /h/ sound on the spoken English language in non-native contexts: a case study of sagamu local government primary school teachers. Eur J Sci Res 154(3):294–300

    Google Scholar 

  19. Kenzhigozhina K, Nurmuhametova K, Berkutbayeva M et al (2020) Phonetic features of spoken English and Kazakh languages (theoretical and experimental research). XLinguae 13(2):166–175

    Article  Google Scholar 

  20. Rapaji V (2020) Lexical stress patterns in high-frequency words of spoken English. Philologia 18(18):1–14

    Article  Google Scholar 

  21. Fujinaga M, Kato T, Suzuki K (1992) An implementation method of IN functional entities on top of distributed operating system and its performance evaluation using experimental system. Kaku Igaku the Japanese J Nuclear Med 19(5):765–766

    Google Scholar 

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Acknowledgements

1. Teaching Research Project of Anhui Agricultural University in 2020 under Grant No. 2020aujyxm90.2. Philosophy and Social Science Funding Project of Anhui Agricultural University in 2021 under Grant No. 2021sk09.

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Correspondence to Yao Fang.

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Fang, Y. Design of Oral English Intelligent Evaluation System Based on DTW Algorithm. Mobile Netw Appl 27, 1378–1385 (2022). https://doi.org/10.1007/s11036-022-01925-7

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