Prosodic boundary prediction from text remains underexplored for morphologically rich languages such as Hindi. We present a comprehensive study of text-based prosodic boundary prediction for Hindi children’s texts using interpretable linguistic representations. We introduce a new corpus of 54 textbook passages annotated by six fluent Hindi speakers and formulate two complementary tasks: Prosodic Boundary Detection (PBD) and Forbidden Pause Detection (FPD). Analysis of the corpus shows that 42.6% of majority-supported boundaries lack explicit punctuation, motivating the use of richer linguistic features beyond surface cues. Using a 28-feature linguistic representation, our models achieve F₁ scores of 0.89 (PBD) and 0.93 (FPD), while generalizing to an independently annotated Hindi corpus without retraining. We study the optimal feature set in terms of its differences from a previously published similar work in English, and also investigate the cross-lingual portability of the feature sets to draw insights that are supported by the structural differences between Hindi and English.

Reading fluency in any language requires accurate word decoding but also natural prosodic phrasing i.e the grouping of words into rhythmically and syntactically coherent units. This holds for, both, reading aloud and silent reading. While adults pause meaningfully at clause or punctuation boundaries, children aged 8-13 often insert inappropriate pauses due to limited breath control and underdeveloped prosodic awareness. We present a text-based model to predict cognitively appropriate pause locations in children’s reading material. Using a curated dataset of 54 leveled English stories annotated for potential pauses, or prosodic boundaries, by 21 fluent speakers, we find that nearly 30% of pauses occur at non-punctuation locations of the text, highlighting the limitations of using only punctuation-based cues. Our model combines lexical, syntactic, and contextual features with a novel breath duration feature that captures syllable load since the last major boundary. This cognitively motivated approach can model both allowed and “forbidden” pauses. The proposed framework supports applications such as child-directed TTS and oral reading fluency assessment where the proper grouping of words is considered critical to reading comprehension.

We present a new Hindi text-to-speech (TTS) dataset and demonstrate its utility for the expressive synthesis of children’s audio stories. The dataset comprises narration by a single female speaker who modifies her voice to produce different story characters. Annotation for dialogue identification, character labelling, and character attribution are provided, all of which are expected to facilitate the learning of character voice and speaking styles. Experiments are conducted using different versions of the annotated dataset that enable training a multi-speaker TTS model on the single-speaker data. Subjective tests show that the multi-speaker model improves expressiveness and character voice consistency compared to the baseline single-speaker TTS. With the multi-speaker model, objective evaluations show comparable word error rates, better speaker voice consistency, and higher correlations with ground-truth emotion attributes. We release a new 16.8 hours storytelling speech dataset in Hindi and propose effective solutions for expressive TTS with narrator voice modulation and character voice consistency.