Deep Phonetic Learning for Kannada Speech Recognition: A Novel Approach Integrating Deep Learning Models with Phonetic Representations

Abstract Speech recognition in regional languages like Kannada presents significant challenges due to phonetic intricacies and the scarcity of resources. This paper introduces a novel approach that leverages deep learning models enhanced with a phonetic framework for effective Kannada speech recognition. A hybrid architecture is proposed, combining the strengths of deep neural networks with phonetic representations to capture the nuanced phonetic structure of Kannada. The study involves designing and integrating a deep learning model specifically tailored to Kannada phonetics, enabling the system to discern subtle phonetic variations critical for accurate recognition. A comprehensive evaluation is conducted using a diverse dataset of Kannada speech samples, demonstrating the model’s robustness across varied speakers, environments, and linguistic contexts. Key innovations include the incorporation of phonetic embeddings, which empower the model to learn and exploit inherent phonetic patterns. The results highlight the system’s effectiveness in real-world scenarios, underscoring its potential for broader applications in regional language processing. This research contributes significantly to advancing speech recognition technology for Kannada, fostering improved human-computer interaction and accessibility within India’s linguistically diverse landscape. Moreover, the proposed deep phonetic learning approach is adaptable to other languages facing similar phonetic challenges, paving the way for future developments in regional language processing.