Reinforcement Learning for grapheme-to-Phoneme Conversion in Kannada Speech Synthesis

Abstract

Grapheme-to-phoneme (G2P) mapping plays a vital role in the development of text-to-speech systems, particularly for languages with complex morphology and limited computational resources such as Kannada. Existing G2P techniques based on handcrafted rules or supervised machine learning depend heavily on linguistic knowledge or large volumes of labeled data, making them difficult to scale for low-resource languages. To address these challenges, this work introduces a reinforcement learning–driven approach for Kannada grapheme-to-phoneme conversion. The task is modeled as a stepwise decision process in which an intelligent agent incrementally predicts phoneme sequences from written text by learning an optimal policy guided by a reward function that reflects pronunciation correctness and phonological coherence. By learning through interaction rather than direct supervision, the proposed framework adapts effectively to novel word forms and pronunciation variations. Experimental evaluation on a Kannada text dataset shows that the reinforcement learning model produces more accurate phoneme sequences and lower error rates when compared to conventional rule-based and statistical G2P methods. These findings demonstrate the potential of reinforcement learning as a flexible and data-efficient solution for building robust G2P systems in low-resource Indian languages, ultimately enhancing the clarity and naturalness of synthesized Kannada speech.