Software de reconocimiento de voz para mejorar la pronunciación del inglés

Autores/as

DOI:

https://doi.org/10.15381/lengsoc.v23i2.26074

Palabras clave:

autonomía, enseñanza de lenguas, pronunciación, retroalimentación, software de reconocimiento de voz

Resumen

A través de los avances tecnológicos han surgido desafíos y oportunidades en diferentes campos. Las aulas de idiomas son algunos de los escenarios más impactados, puesto que los estudiantes han logrado integrar recursos tecnológicos en su proceso de aprendizaje. Uno de los aspectos en los que la tecnología ha demostrado ser relevante para el aprendizaje de lenguas es la pronunciación. Aunque la instrucción y la retroalimentación en este ámbito son cruciales, el tiempo disponible en el aula para su desarrollo suele ser limitado. Por esta razón, en este estudio se analiza  el impacto del uso del software de reconocimiento de voz (SRV) como herramienta complementaria para la enseñanza de la pronunciación del inglés como lengua extranjera (ILE). Metodológicamente, el diseño es cualitativo. La muestra está constituida por 10 estudiantes del nivel básico de ILE de un centro de idiomas de una universidad en Bogotá, Colombia. Los datos se recopilaron por medio de notas de campo, grabaciones de audio y video, así como mediante los diarios de los estudiantes. Entre los resultados, se encontró que la retroalimentación del SVR contribuye a la mejora de la pronunciación de los estudiantes; además, motiva su compromiso con el aprendizaje y promueve su autonomía.

Biografía del autor/a

  • Laura Sánchez, Universidad El Bosque, Bogotá, Colombia

    Is an associate instructor at Universidad El Bosque in Bogotá, Colombia. She holds a bachelor's degree in Bilingual Education with an emphasis on English teaching from Universidad El Bosque, and a master's degree in education from the same institution. She has participated in the creation of online EFL learning material at the language center of Universidad El Bosque. Her research interests include dynamic assessment, teaching and learning methods on English as a foreign language, and ICT for educational purposes.

  • Andrés Morales, Universidad El Bosque, Bogotá, Colombia

    Works as an academic coach at Santillana in Bogotá, Colombia. He completed his undergraduate degree in modern languages at Universidad Javeriana, and a postgraduate program in university teaching at Universidad El Bosque. With over 10 years of experience as an English teacher in various settings, including universities and language academies, he is enthusiastic about combining technology with teaching.

  • Ingrid Rodríguez, Universidad El Bosque, Bogotá, Colombia

    is an associate Professor at Universidad El Bosque in Bogotá, Colombia. She holds a bachelor's degree in Spanish and English from Universidad Pedagógica Nacional, and a master’s degree in Applied Linguistics from Universidad Distrital Francisco José de Caldas. She is an active member of the research group Educación e Investigación Unbosque and directs a research hotbed called Equilateral Knowledge from the same institution. Her research interests include beliefs and reflections, academic writing, teaching methodologies and ICT for educational purposes.

Referencias

Ahmad, Y. (2018). Teaching English Pronunciation of Suprasegmental Features on Students of English Education. SHS Web of Conferences, (42), 1-5. https://doi.org/10.1051/shsconf/20184200048

Asratie, M., Wale, B., and Aylet, Y. (2023). Effects of using educational technology tools to enhance EFL students’ speaking performance. Education and Information Technologies, (28), 1-21. https://link.springer.com/article/10.1007/s10639-022-11562-y

Bogdan, R., and Biklen, S. (1997). Qualitative Research in Education. Allyn & Bacon.

Brookhart, S. M. (2008). How to Give Effective Feedback to Your Students. Association for Supervision & Curriculum Development. https://ebookcentral-proquest-com.ezproxy.unbosque.edu.co/lib/bibliobosque-ebooks/detail.action?docID=361035

Bryman, A. (2012). Social Research Methods (4th ed.). Oxford University Press.

Bryant A., and Charmaz, K. (2007). Grounded theory research: Methods and practices. In A. Bryant and K. Charmaz (Eds.), The Sage handbook of Grounded Theory (pp. 1-28). Sage Publications. https://doi.org/10.4135/9781848607941

Çelik, S., and Baran, E. (2022). Student response system: its impact on EFL students’ vocabulary achievement. Technology, Pedagogy and Education, 31(2), 141-158. https://doi.org/10.1080/1475939X.2021.1986125

Çetin, E. (2021). Digital storytelling in teacher education and its effect on the digital literacy of pre-service teachers. Thinking Skills and Creativity, (39), 100760. https://www.tandfonline.com/doi/full/10.1080/1475939X.2021.1986125

Council of Europe. (2022). Common European Framework of Reference for Languages: Learning, teaching, assessment – Companion volume. Council of Europe Publishing. www.coe.int/lang-cefr

Derwing, T., and Munro, M. (2015). Pronunciation Fundamentals: Evidence-based Perspectives for L2 Teaching and Research. John Benjamins Publishing Company.

Evers, K., and Chen, S. (2021). Effects of Automatic Speech Recognition Software on Pronunciation for Adults with Different Learning Styles. Journal of Educational Computing Research, 59(4), 669-685. https://journals.sagepub.com/doi/abs/10.1177/0735633120972011

Ferrando, E. (2023). La motivación en la enseñanza de segundas lenguas: aproximación teórico-práctica en el ámbito del enfoque integrado AICLE. Lengua y Sociedad, 22(1), 117-137. https://doi.org/10.15381/lengsoc.v22i1.23650

Hazen, H. (2020). Use of oral examinations to assess student learning in the social sciences. Journal of Geography in Higher Education, 44(4), 592-607. https://www.tandfonline.com/doi/abs/10.1080/03098265.2020.1773418?journalCode=cjgh20

Kelly, G. (2000). How to Teach Pronunciation. Longman.

Levis, J., and Suvorov, R. (2012). Automatic Speech Recognition. In C. Chapelle (Ed.), The Concise Encyclopedia of Applied Linguistics. Wiley-Blackwell. https://doi.org/10.1002/9781405198431.wbeal0066.pub2

Macklem, G. (2015). Boredom in the classroom: Addressing Student Motivation, Self-regulation, and Engagement in Learning. Springer International Publishing. https://doi.org/10.1007/978-3-319-13120-7

Moccozet, L. (2012, July). Introducing Learning Performance in Personal Learning Environments. 2012 IEEE 12th International Conference on Advanced Learning Technologies. https://doi.org/10.1109/icalt.2012.75

Plailek, T., and Essien, A. (2021). Pronunciation problems and factors affecting English pronunciation of EFL students. Turkish Journal of Computer and Mathematics Education, 12(12), 2026-2033. http://ezproxy.unbosque.edu.co:2048/login?url=https://www.proquest.com/scholarly-journals/pronunciation-problems-factors-affecting-english/docview/2622819321/se-2

Meilani, F., Sukiyadi, D., and Purnawarman, P. (29 December 2021). Online Individual Corrective Feedback: English Language Learners’ Beliefs in Pronunciation Learning. In Proceedings of the International Conference on Sustainable Innovation Track Humanities Education and Social Sciences (ICSIHESS 2021) (pp. 9-14). Atlantis Press. https://www.atlantis-press.com/proceedings/icsihess-21/125967893

Syauqi, K., Munadi, S., and Triyono, M. (2020). Students’ Perceptions toward Vocational Education on Online Learning during the COVID-19 Pandemic. International Journal of Evaluation and Research in Education, 9(4), 881-886. http://doi.org/10.11591/ijere.v9i4.20766

Stratton, S. (2021). Population Research: Convenience Sampling Strategies. Prehospital and Disaster Medicine, 36(4), 373-374. https://doi.org/10.1017/S1049023X 21000649

Vahdany, F., Divsar, H., and Alem, M. (2022). The Interface between Pronunciation Learning Strategies (PLS) and Pronunciation Achievement. Iranian Journal of English for Academic Purposes, 11(2), 56-73. https://journalscmu.sinaweb.net/article_154305_a2bb683213798fd5cf858b86081681c7.pdf

Wang, B., Teo, T., and Yu, S. (2017). Teacher feedback to student oral presentations in EFL classrooms: a case study. Journal of Education for Teaching, 43(2), 262-264. https://doi.org/10.1080/02607476.2016.1257507

Yokota, Y., Iwatsubo, T., Takeuchi, T., Hakoda, A., Nakagawa, Y., Kawabata, K., Inoue, Y., Miyamoto, H., Ikeo, K., Kojima, Y., Miyazaki, J., Abe, T., and Higuchi, K. (2022). Effects of a novel endoscopic reporting system with voice recognition on the endoscopic procedure time and report preparation time: propensity score matching analysis. Journal of Gastroenterology, 1-9. https://link.springer.com/article/10.1007/s00535-021-01835-7

Zuber-Skerritt, O., and Wood, L. (Eds.). (2019). Action Learning and Action Research: Genres and Approaches. Emerald Publishing Limited. https://doi.org/10.1108/9781787695375

Descargas

Publicado

2024-12-30

Número

Sección

Dossier sobre inteligencia artificial, lenguaje y discurso digital

Cómo citar

Sánchez Salgado, L. V., Morales Chocontá, M. A., & Rodríguez Granados, I. J. (2024). Software de reconocimiento de voz para mejorar la pronunciación del inglés. Lengua Y Sociedad, 23(2), 963-983. https://doi.org/10.15381/lengsoc.v23i2.26074