VOICE RECOGNITION AI IN MUSIC EDUCATION PLATFORMS
Keywords:
Voice Recognition AI, Music Education Platforms, Vocal Itch Analysis, Rhythm Assessment, Intelligent Tutoring Systems, Digital Music PedagogyAbstract [English]
Voice recognition artificial intelligence (AI) can be described as a radical technology in music education platforms, which allows personalized, data-driven and scalable experiences in vocal training. The conventional method of music pedagogy is based on teacher-directed feedback, which is sometimes time-consuming, subjective, and hard to compare with different groups of learners. Conversely, systems that use voice recognition take advantage of the developments in signal processing, machine learning, and deep neural networks to recognize vocal pitch, timbre, rhythm, articulation, and pronunciation with high-temporal resolution. This paper gives an in-depth approach to the incorporation of voice recognition AI into music education platforms in terms of system architecture, approach to method, and impact on education. The suggested method is inclusive of strong audio data capture, noise sensitive preprocessing, and feature responses including Mel-frequency cepstral coefficients, pitch contours to characterize musical voice. These are supervised and deep learning-based recognition models that are used to measure the performance of the voice and provide real-time corrective feedback. Practical testing shows that AI-enhanced systems are more accurate and responsive with regards to pitch correction, rhythm matching, and diction measurement than traditional methods of teaching, and enhance engagement and independent practice in learners. In addition to performance benefits, the study indicates the pedagogical benefits of continuous feedback, adaptation in difficulty and the objective assessment.
References
Ahlawat, H., Aggarwal, N., and Gupta, D. (2025). Automatic Speech Recognition: A Survey of Deep Learning Techniques and Approaches. International Journal of Cognitive Computing Engineering, 7, 201–237. https://doi.org/10.1016/j.ijcce.2024.12.007
Ahmed, M. M. (2024). Resources’ Identification in Education Systems. Journal of Digital Security and Forensics, 1(1), 1–11. https://doi.org/10.29121/digisecforensics.v1.i1.2024.12
Du, W., Maimaitiyiming, Y., Nijat, M., Li, L., Hamdulla, A., and Wang, D. (2023). Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview. Applied Sciences, 13, 326. https://doi.org/10.3390/app13010326
Kapyshev, G., Nurtas, M., and Altaibek, A. (2024). Speech Recognition for Kazakh Language: A Research Paper. Procedia Computer Science, 231, 369–372. https://doi.org/10.1016/j.procs.2023.12.219
Karabaliyev, Y., and Kolesnikova, K. (2024). Kazakh Speech and Recognition Methods: Error Analysis and Improvement Prospects. Scientific Journal of Astana IT University, 20, 62–75. https://doi.org/10.37943/20DZGH8448
Kozhirbayev, Z., and Islamgozhayev, T. (2023). Cascade Speech Translation for the Kazakh Language. Applied Sciences, 13, 8900. https://doi.org/10.3390/app13158900
Liu, Y., Yang, X., and Qu, D. (2024). Exploration of Whisper Fine-Tuning Strategies for Low-Resource ASR. EURASIP Journal on Audio, Speech, and Music Processing, 2024, 29. https://doi.org/10.1186/s13636-024-00349-3
Mamyrbayev, O., Oralbekova, D., Alimhan, K., Turdalykyzy, T., and Othman, M. (2022). A Study of Transformer-Based end-to-end Speech Recognition System for Kazakh Language. Scientific Reports, 12, 8337. https://doi.org/10.1038/s41598-022-12260-y
Mukhamadiyev, A., Mukhiddinov, M., Khujayarov, I., Ochilov, M., and Cho, J. (2023). Development of Language Models for Continuous Uzbek Speech Recognition System. Sensors, 23, 1145. https://doi.org/10.3390/s23031145
Mussakhojayeva, S., Dauletbek, K., Yeshpanov, R., and Varol, H. A. (2023). Multilingual Speech Recognition for Turkic Languages. Information, 14, 74. https://doi.org/10.3390/info14020074
Oyucu, S. (2023). A Novel end-to-end Turkish Text-To-Speech (TTS) System Via Deep Learning. Electronics, 12, 1900. https://doi.org/10.3390/electronics12081900
Polat, H., Turan, A. K., Koçak, C., and Ulaş, H. B. (2024). Implementation of A Whisper Architecture-Based Turkish ASR System and Evaluation of Fine-Tuning with LoRA Adapter. Electronics, 13, 4227. https://doi.org/10.3390/electronics13214227
Rakhimova, D., Duisenbekkyzy, Z., and Adali, E. (2025). Investigation of ASR Models for Low-Resource Kazakh Child Speech: Corpus Development, Model Adaptation, and Evaluation. Applied Sciences, 15(16), 8989. https://doi.org/10.3390/app15168989
Veitsman, Y., and Hartmann, M. (2025). Recent Advancements and Challenges of Turkic Central Asian Language Processing. In Proceedings of the First Workshop on Language Models for Low-Resource Languages (309–324). Association for Computational Linguistics.
Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., Meng, Z., Hu, K., Rosenberg, A., Prabhavalkar, R., Park, D. S., Haghani, P., Riesa, J., Perng, G., Soltau, H., Strohman, T., Ramabhadran, B., Sainath, T., Moreno, P., Chiu, C.-C., Schalkwyk, J., Beaufays, F., and Wu, Y. (2023). Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages. arXiv.
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Copyright (c) 2026 Sunil Damodar Rathod, Nidhi Tewatia, Dr. Srijita Bhattacharjee, Nivetha N, Amruta Prasad Kharade, Shikha Verma Kashyap
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