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arXiv · 2609.19324

PersianVox: A Prosody-Aware Approach for Speech Dataset Generation from In-the-Wild Data

Saeedreza Zouashkiani·Soheil Khalesi·Saman Soleimani Roudi·Sajjad Amini·Shahrokh Ghaemmaghami
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Abstract

Advancement of zero-shot text-to-speech synthesis is currently hindered for low-resource languages by the scarcity of large-scale, high-fidelity speech datasets. Traditional alignment-based methods require rare verbatim transcripts, while standard in-the-wild pipelines often rely on single-model automatic speech recognition and silence-based segmentation, leading to transcription errors and truncated prosody. To address these challenges for the Persian language, this paper introduces PersianVox, a fully automated pipeline designed to generate high-quality speech corpora from unlabeled web data. Our approach integrates a novel prosody-aware segmentation strategy that utilizes acoustic turn-detection to preserve linguistic completeness and optimize utterance duration for long-context modeling. Furthermore, we employ a dual-model agreement mechanism, leveraging two distinct model architectures to filter unreliable transcriptions without ground truth. This pipeline yields a 2,400-hour multi-speaker dataset, the largest open-source speech resource available for Persian to date. Additionally, we provide the first comparative benchmark of speech quality assessment methods for Persian, releasing a human-annotated subset to facilitate future research.

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BibTeXRIS

Saeedreza Zouashkiani, Soheil Khalesi, Saman Soleimani Roudi, Sajjad Amini, Shahrokh Ghaemmaghami. 2026-09-16. PersianVox: A Prosody-Aware Approach for Speech Dataset Generation from In-the-Wild Data. https://arxiv.org/abs/2609.19324

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