Year: 2026
Type: conference-paper
Abstract: Recent advances in text-to-speech (TTS) have been driven by large, multi-domain speech corpora, yet the expressive potential of audiobook data remains underexamined.We argue that human-narrated audiobooks, particularly fictional works, contain rich and diverse prosodic cues arising from the natural alternation between neutral narration and expressive character dialogue.Building from this observation, we introduce LibriQuote, a large-scale 5.3K hours of expressive speech drawn from character quotations.Each quote is supplemented with contextual pseudo-labels for speech verbs and adverbs that characterize the intended delivery of direct speech (e.g., "he whispered softly").We found that fine-tuning a flowmatching model on LibriQuote yields substantial improvements in expressivity and intelligibility, while training from scratch enhances expressiveness of an autoregressive TTS model.Benchmarking on LibriQuote-test highlights significant variability across systems in generating expressive speech.We publicly release the dataset, code, and evaluation resources to facilitate reproducibility.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166837591