Context-Aware Lexical Stress Prediction and Phonemization for U krainian TTS Systems (2025)

Abstract: Text preprocessing is a fundamental component of high-quality speech synthesis. This work presents a novel rule-based phonemizer combined with a sentence-level lexical stress prediction model to improve phonetic accuracy and prosody prediction in the text-to-speech pipelines. We also introduce a new benchmark dataset with annotated stress patterns designed for evaluating lexical stress prediction systems at the sentence level.Experimental results demonstrate that the proposed phonemizer achieves a 1.23% word error rate on a manually constructed pronunciation dataset, while the lexical stress prediction pipeline shows results close to dictionary-based methods, outperforming existing neural network solutions.

## introduction text-to-speech (tts) systems are essential for enhancing human-computer interaction across various everyday applications, including virtual assistants, language learning tools, and navigation systems, while making digital content more accessible to people with visual impairments. the quality of tts output depends heavily on accurate linguistic analysis, especially for languages with rich morphology like ukrainian. effective text preprocessing is a critical step in language modeling pipelines, helping models generalize from limited data by transforming raw input into a standardized format @xcite . this reduces linguistic variability and improves consistency. while similar results can be achieved without preprocessing, such approach typically requires significantly larger datasets and forces the model to learn a broader range of morphological and phonological irregularities, often at the cost of performance and interpretability. moreover, post-training adjustments such as refining pronunciation or stress patterns become difficult without retraining or fine-tuning the entire model. phonemization and lexical stress prediction are two areas where preprocessing can significantly enhance tts quality. ukrainian, in particular, poses unique challenges due to its complex phonology and non-deterministic stress system @xcite . the language features rich inflectional morphology, frequent sound changes, such as consonant cluster reductions and different types of assimilation. moreover, ukrainian has a non-deterministic stress system, where lexical stress may be fixed in some word forms, but in other cases varies based on syntactic or morphological context, influenced by factors such as free variation, where multiple stress placements are correct without a change in meaning (e.g., байдуже vs. байдуже -"indifferently"); heteronyms, where identical spellings have different meanings depending on stress (e.g., замок -"castle" vs. замок -"lock"); and inflectional stress shifts, where morphological changes like case or number alter stress placement (e.g., низовини -nominative plural vs. низовиниgenitive singular, both meaning "lowlands"). apart from that, phonemization is an essential preprocessing step that allows text-to-speech models to create speech from phoneme-based text, improving the match between text and audio data. this means that the quality of generated speech directly depends on the accurate mapping of graphemes to phonemes. these complexities make accurate stress prediction and phonemization essential for naturalsounding speech synthesis. in this work, we propose a framework for ukrainian in which we introduce: a benchmark dataset for evaluating the performance of existing stress prediction systems; a context-aware model for lexical stress prediction; and a new rule-based phonemizer designed to reflect the unique phono-logical characteristics of ukrainian. the benchmark, datasets, and source code are available at the following link: https://github. com/lang-uk/ukrainian-tts-preprocessing . ## conclusion in this work, we presented a modular approach to ukrainian text-to-speech preprocessing that combines a rule-based phonemizer with a contextaware neural model for lexical stress prediction. our system achieves strong results in both tasks: it reaches a low word error rate of 1.23% on a constructed phonemization dataset and shows competitive performance in lexical stress disambiguation, outperforming existing neural models and closely matching dictionary-based approaches. as part of this work, we also released the first publicly available benchmark dataset for evaluating ukrainian lexical stress at the sentence level, providing a standardized foundation for consistent evaluation and future research.