Disambiguation resolution in speech synthesis is one of the main challenges in text-to-speech conversion. Machine learning methods and artificial neural networks have been successfully applied to this problem in synthesis systems for English, Spanish, and other common languages. For low-resource languages, the available data are insufficient to train artificial neural networks, so heuristic methods for context analysis and selection of the correct homonym for polysemantic words should be used. The purpose of this study is to develop a word sense disambiguation (WSD) method for the low-resource Chechen language and to introduce it into a speech synthesis system. The study presents the developed method and three algorithms: AWEN (based on Euclidean distance), AWA (weighted average), and AWN (weighted normalized distance) for word sense disambiguation. A corpus of Chechen texts, CheWSData, was compiled, containing 15,035 manually selected sentences derived from 5 million annotated words and reflecting the natural frequency of polysemy across grammatical categories. Experimental results show that the proposed AWN method achieves the best performance, with an F1-score of 0.78 and an accuracy of 0.80, outperforming AWA (F1: 0.74) and AWEN (F1: 0.40). For specific parts of speech, AWN reaches F1-scores of 0.82 for nouns, 0.83 for verbs, and 0.85 for adverbs. Comparative analysis with existing WSD methods for low-resource languages (Kashmiri, Hausa, Assamese, Urdu, and Vietnamese) demonstrates that AWN is competitive, ranking second after ViConBERT (F1: 0.87) and ahead of XLM-R for Hausa (F1: 0.79). The developed software module for homonym recognition was integrated into the Chechen speech synthesis system, contributing to more natural synthesized speech.
Izrailova et al. (Mon,) studied this question.