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Размышление о дизайне и восприятии пользователями приложения Tamil talk

https://doi.org/10.15514/ISPRAS-2021-33(1)-13

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Аннотация

Tamil talk – это приложение для преобразования устной речи в текст, разработанное с позиций языка и философии. В этой статье используется автохтонный подход к осмыслению дизайна и принятия пользователями приложения Tamil talk на основе анализа литературных источников. Используется междисциплинарный подход и исследуется влияние таких факторов, как смена языка, приверженность языку и философия в контексте принятия пользователем преобразования устной речи в текст. Как полагают авторы литературных источников, такое приложение может импонировать части носителей тамильского языка, но имеются сложные проблемы, которые требуют дальнейшего исследования. Дальнейшие исследования должны быть направлены на разработку приложения, соответствующего концептуальной модели и испытываемого большим числом носителей языка, чтобы прийти к более точному пониманию принятия пользователями этого приложения.

Об авторах

Радж РАМАЧАНДРАН СУБРАМАНЬЯН
Университет Западной Англии
Великобритания

Кандидат наук, преподаватель компьютерных наук на факультете окружающей среды и технологий



Эммануэль Кайоде Акиншола ОГУНШИЛЕ
Университет Западной Англии
Великобритания

Кандидат наук, старший преподаватель компьютерных наук



Список литературы

1. Rabiner L.A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, vol. 77, no. 2, 1989, pp. 257–286.

2. Mann D., Frederic K. Weston N., Ramachandran R. & Ogunshile E Tamil talk: What you speak is what you get! In Proc. of the 7th International Conference in Software Engineering Research and Innovation (CONISOFT), 2019, pp. 36-48.

3. Ramachandran R. Predicting user acceptance of Tamil speech to text by native Tamil Brahmans. Doctoral dissertation. Sheffield Hallam University, 2018, 233 p.

4. Keane E. Tamil. Journal of the International Phonetic Association, vol. 34, issue 1, 2004, pp. 111-116.

5. Srinivasan A., Srinivasa K., Kannan K., & Narashimhan D. Speech Recognition of the letter "zha" in Tamil Language using HMM. International Journal of Engineering Science and Technology, vol.1, no. 2, 2009, pp 67-72.

6. Shulman D. Tamil. Harvard University Press, 2016, 416 p.

7. Srinivasan A. Real time speaker recognition of letter 'zha' in Tamil language. In Proc. of the Fourth International Conference on Computing, Communications and Networking Technologies, 2013, pp. 1-5.

8. Crystal D. A little book of language (Little Histories). UNSW Press, 2010, 272 p.

9. Boyd C. Speech Recognition Technology: The Past, Present, and Future. Available at https://medium.com/swlh/the-past-present-and-future-of-speech-recognition-technology-cf13c179aaf, accessed March 12, 2020.

10. Fu T., Gao S., Wu X. Improving Minority Language Speech Recognition Based on Distinctive Features. Lecture Notes in Computer Science, vol 11266, 2018, pp. 411-420.

11. Pandharipande R. Minority Matters: Issues in Minority Languages in India. International Journal on Mulitcutltural Socieitis (IJMS), vol 4, no. 2, 2002, pp 213-234.

12. Steever S. Introduction to the Dravidian Languages. In Steever S. (ed.). The Dravidian Languages. London: Routledge, 1998, pp. 1–39/

13. Zvelebil K. Companion studies to the history of Tamil literature. Leiden: Brill, 1992, 291 p.

14. Lo L. Tamil. Ancient Scripts. Available at http://www.ancientscripts.com/tamil.html, accessed December 1, 2019.

15. Argondizzo P. How Accurate is Google Translate in 2018? Available at https://www.argotrans.com/blog/accurate-google-translate-2018/, accessed March 10, 2020.

16. Devarajan S. Relationship between Japanese and Dravidian (Tamil). Available at http://japanese-dravidian.blogspot.com/2009/01/relationship-between-japanese-and.html, accessed March 10, 2020.

17. Deng Y., Li X., Kwan C. et al. Continuous Feature Adaptation for Non-Native Speech Recognition. International Journal of Computer, Information Science and Engineering, vol:1, no:6, 2007, pp. 1701-1708.

18. Pornpanomchai C., Ngamwongsakoller P., Tangpitaksame P. & Wonvattanakij C. Thai-Speech-to-Text Transformation Using Dictionary-Based Technique. In Proc. of the International Conference on Networks and Information, 2012, pp. 65-69.

19. Gales M., Young S. The Application of Hidden Markov Models in Speech Recognition. Foundation and Trends in Signal Processing, 2007, pp. 195-304.

20. Blanken H., de Vries A., Blok H. & Feng L. (eds.) Multimedia Retrieval (Data-Centric Systems and Applications). Springer Science & Business Media, 2007, 390 p.

21. Kreyssig F. Deep Learning for User Simulation in a Dialogue System. Master Thesis, University of Cambridge, 2018, 48 p.

22. Stuttle M. A Gaussian Mixture Model Spectral Representation for Speech Recognition. PhD Thesis, Cambridge University Engineering Department, 2003, 163 p.

23. Duran N. & Battle S. Probabilistic Word Association for Dialogue Act Classification with Recurrent Neural Networks. Communications in Computer and Information Science, vol. 893, 2018, pp. 229-239.

24. Graves A., Fernandez S., Gomez F. & Schmidhuber J. Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. In Proc. of the 23rd International Conference on Machine learning, 2006, pp. 369–376.

25. Yu D., Deng L. Deep Neural Network-Hidden Markov Model Hybrid Systems. In Automatic Speech Recognition. Signals and Communication Technology. Springer, London, 2015, pp. 99-116.

26. Clarke S. Measuring API Usability. Dr. Dobb’s Journal, vol. 29, 2004, pp. S6-S9.

27. Samudravijaya K. & Barol M. Comparison of Public Domain Software Tools for Speech Recognition. In Proc. of the Workshop on Spoken Language Processing, 2003, pp. 125–131.

28. Lange P. & Suendermann-Oeft D. Tuning Sphinx to Outperform Google’s Speech Recognition API. In Proc. of the Conference on Electronic Speech Signal Processing (ESSV 2014), 2014, pp. 32-41.

29. Matarneh R., Maksymova S., Lyashenko S., & Belova N. Speech Recognition Systems: A Comparative Review. IOSR Journal of Computer Engineering, vol. 19, issue 5, 2017, pp. 71-79.

30. Atwood J. Don’t Reinvent the Wheel, Unless you Plan on Learning More About Wheels. Available at https://blog.codinghorror.com/dont-reinvent-the-wheel-unless-you-plan-on-learning-more-about-wheels/, accessed March 10, 2020.

31. Kepuska V. & Bohouta G. Comparing Speech Recognition Systems (Microsoft API, Google API and CMU Sphinx). International Journal of Engineering Research and Application, vol. 7, issue 3, Part-2, 2017, pp. 20-24.

32. Lardinois F. Google 1launches an improved speech-to-text service for developers. Available at https://techcrunch.com/2018/04/09/google-launches-an-improved-speech-to-text-service-for-developers/, accessed March 10, 2020.

33. Techseen Bureau. Google Cloud Speech API now supports 30 more languages. Available at https://techseen.com/2017/08/14/google-cloud-speech-api-update/, accessed March 10, 2020.

34. Novet J. Google says its speech recognition technology now has only an 8% word error rate. Available at https://venturebeat.com/2015/05/28/google-says-its-speech-recognition-technology-now-has-only-an-8-word-error-rate/, accessed March 10, 2020.

35. Juang B.H., & Rabiner L.R. Automatic speech recognition – A brief history of the technology development. In Elsevier Encyclopaedia of Language and Linguistics, 2005, pp. 806–819.

36. Walker W., LamereP., Kwok, P. et al. Sphinx-4: A flexible open source framework for speech recognition. SMLI TR2004-0811, Sun Microsystems Laboratories, 2004. 9 p.

37. Twiefel J., Baumann T., Heinrich S., & Wermte, S. Improving domain independent cloud-based speech recognition with domain-dependent phonetic post-processing. In Proc. of the 28th AAAI Conference on Artificial Intelligence, 2014, pp. 1-7.

38. Ashwell T. & Elam J. How accurately can Google Web Speech API recognize and transcribe Japanese L2 English learners’ oral production? The JALT CALL Journal, vol. 13, vo.1, 2017, pp 59-76.

39. Беленко М.В., Балакшин П.В. Сравнительный анализ систем распознавания речи с открытым кодом. Международный научно-исследовательский журнал, вып, 4 (58) часть 4, 2017 г., стр. 13-18. / Belenko M.V. & Balakshin P.V. Comparative Analysis of Speech Recognition Systems with Open Code. International Research Journal, issue: № 04 (58) Part 4, 2017, pp. 13-18 (in Russian).

40. Kamarudin M.R., Yusof M.A.F.M., & Jaya H.T. Low cost smart home automation via Microsoft speech recognition. International Journal of Engineering & Computer Science, vol. 13, no. 3, 2013, pp. 6-11.

41. Lacoma T. How to set up speech-to-text in Windows 10. Available at https://www.digitaltrends.com/computing/how-to-set-up-speech-to-text-in-windows-10/, accessed March 10, 2020.

42. Manaswi N. Deep Learning with Applications Using Python. Apress, 2018, 219 p.

43. Tschacher T. From script to language: the three identities of ‘Arabic-Tamil’. South Asian History and Culture, vol. 9, no. 1, 2018, pp. 16-37.

44. Fuller C.J., & Narasimhan H. Tamil Brahmans: The making of a middle-class caste. University of Chicago Press, 2014, 288 p.

45. Зиеп Н.Н., Жданов А.А. Нейроноподобный подход к распознаванию речи. Программирование, том 44, no. 3, 2018 г., стр. 49-62 / Diep N.N. & Zhdanov A.A. Neuron-Like Approach to Speech Recognition. Programming and Computer Software, vol. 44, no. 3, 2018, pp. 170-180.

46. Saravanan V., Lakshmi S., & Caleon I.S. The debate over literary Tamil versus standard spoken Tamil: What do teachers say? Journal of Language, Identity, and Education, vol. 8, no. 4, 2009, pp. 221-235.

47. Wee, C. L. The Indigenized West in Asian multicultures: literary-cultural production in Malaysia and Singapore. International Journal of Postcolonial Studies, vol. 10, no. 2, 2008, pp. 188-206.

48. Ramazani J., & Ramanujan A.K. Metaphor and postcoloniality: the poetry of AK Ramanujan. Contemporary Literature, vol. 39, no. 1, 1998, pp. 27-53.

49. Sefa Dei G.J., & Asgharzadeh A. Language, education and development: case studies from the Southern contexts. Language and Education, vol. 17, no. 6, 2003, pp. 421-449.

50. Kailasapathy K. The Tamil purist movement: a re-evaluation. Social scientist, vol. 7, no. 10, 1979, pp. 23-51.

51. López C.C. Language is the soul of the nation: Language, education, identity, and national unity in Malaysia. Journal of Language, Identity & Education, vol. 13, no. 3, 2014, pp. 217-223.

52. Schiffman H.F. Standardization or restandardization: The case for “standard” spoken Tamil. Language in Society, vol. 27, no. 3, 1998, pp. 359-385.

53. SlobodaT. & Waibel A. Dictionary Learning for Spontaneous Speech Recognition. In Proc. of the International Conference on Spoken Language Processing, 1996, pp. 2328-2331.

54. Lionnet F. Créolité in the Indian Ocean: Two models of cultural diversity. Yale French Studies, no. 82, 1993, pp. 101-112.

55. Rudisill K. Everyday Flamboyancy in Chennai's "Sabha" Theatre. Asian Theatre Journal, vol. 29, no. 1, 2012, pp. 276-290.

56. Canagarajah A.S. Language shift and the family: Questions from the Sri Lankan Tamil diaspora. Journal of Sociolinguistics, vol. 12, no. 2, 2008, pp. 143-176.

57. Ridge B. National language planning and language shifts in Malaysian minority communities: speaking in many tongues. Amsterdam University Press, 2012, 208 p.

58. Das Neela S. Rewriting the past and reimagining the future: The social life of a Tamil heritage language industry. American ethnologist, vol. 38, no. 4, 2011, pp. 774-789.

59. Das S.N. Between convergence and divergence: Reformatting language purism in the Montreal Tamil diasporas. Journal of Linguistic Anthropology, vol. 18, no. 1, 2008, pp. 1-23.

60. Schiffman H.H.F. Malaysian Tamils and Tamil linguistic culture. Language & Communication, vol. 22, no. 2, 2002, pp. 159-169.

61. Pillai A.D. Language Shift among Singaporean Malayalee Families. Language in India, vol. 9, 2009, pp. 1-25.

62. Kadakara S. Status of Tamil language in Singapore: An analysis of family domain, Education Research and Perspectives, vol. 42, 2015, pp. 25-64.

63. Muniandy M.K., Nair, G.K.S.N., Shanmugam et al. Sociolinguistic competence and Malaysian students' English language proficiency. English Language Teaching, vol. 3, no. 3, 2010, pp. 145-151.

64. Barnes L. English as a global language: An African perspective. Studies in the Languages of Africa, vol. 36, issue 2, 2005, pp. 243-265.

65. Fuller C.J., & Narasimhan H. Traditional vocations and modern professions among Tamil Brahmans in colonial and post-colonial south India. The Indian Economic & Social History Review, vol. 47, no. 4, 2010, pp. 473-496.

66. Coperahewa S. Purifying the Sinhala Language: The Hela Movement of Munidasa Cumaratunga (1930s–1940s). Modern Asian Studies, vol. 46, issue 4, 2012 , pp. 857-891

67. Липаев В.В. Методология верификации и тестирования крупномасштабных программных средств. Программирование, том 29, no. 6, 2003 г., стр. 7-24 / Lipaev V.V. A Methodology of Verification and Testing of Large Software Systems. Programming and Computer Software, vol. 29, no. 6, 2003, pp. 298-309.

68. Фролов А.М. Гибридный подход к повышению надежности программных систем. Программирование, том 30, no. 1, 2004 г., стр. 25-36 / Frolov A.M. A Hybrid Approach to Enhancing the Reliability of Software. Programming and Computer Software, vol. 30, no. 1, 2004, pp. 18-24.


Для цитирования:


РАМАЧАНДРАН СУБРАМАНЬЯН Р., ОГУНШИЛЕ Э. Размышление о дизайне и восприятии пользователями приложения Tamil talk. Труды Института системного программирования РАН. 2021;33(1):189--208. https://doi.org/10.15514/ISPRAS-2021-33(1)-13

For citation:


RAMACHANDRAN SUBRAMANIAN R., OGUNSHILE E. A reflection on the design and user acceptance of Tamil talk. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2021;33(1):189--208. https://doi.org/10.15514/ISPRAS-2021-33(1)-13

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