VOICE RECOGNITION AI IN MUSIC EDUCATION PLATFORMS - Rathod, Sunil Damodar; Tewatia, Nidhi; Bhattacharjee, Srijita; Nivetha N; Kharade, Amruta Prasad; Kashyap, Shikha Verma (ShodhKosh: Journal of Visual and Performing Arts, 2026)
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.