arXiv Computation and Language
Sep 16

PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark

PARSA‑Bench is the first dedicated benchmark for evaluating large audio‑language models on Persian, addressing unique challenges such as classical poetry, traditional music, and code‑switching. It comprises 16 tasks—10 of which are new—covering speech understanding, paralinguistic analysis, and culturally grounded audio reasoning. Across most tasks, text‑only baselines outperform audio‑based models, indicating that audio understanding remains the main limitation, except for Persian poetry where prosody provides additional information that audio beats text.

By Mohammad Javad Ranjbar Kalahroodi, Mohammad Amini, Parmis Bathayan, Heshaam Faili, Azadeh Shakery
arXiv AI
Jun 24

ZONOS2 Technical Report

arXiv:2606. 24320v1 Announce Type: cross Abstract: We present ZONOS2 8B, our latest TTS model, which achieves state-of-the-art naturalness, prosody, and voice cloning fidelity.

By Gabriel Clark, Sofian Mejjoute, Mohamed Osman, George Close, Beren Millidge
arXiv Computation and Language
2d ago

GigaSpeech 3: Scaling Multi-Dialect Arabic Speech Recognition with Weakly Labeled Data

GigaSpeech 3 is a new Arabic speech corpus comprising 39,000 hours and 10 million weakly transcribed segments across Algerian, Egyptian, Iraqi, Saudi, and Syrian dialects. It is built from 60 hours of manual transcription per dialect, using self‑supervised labeling models and noisy‑student refinement to produce high‑quality data. The corpus enables dialect‑specific and multi‑dialect speech recognition models that outperform existing third‑party systems, achieving a macro‑average word error rate of 23.86% for dialect‑specific models and 25.51% for a multi‑dialect model.

By Junzhe Liu, Yujie Tu, Yifan Yang, Peng Wang, Haoran Wang, Guodong Lin, Leyan Liao, Chengquan Zhang, Zhifu Gao, Xiangang Li, Qinyuan Cheng, Xipeng Qiu, Wei-Qiang Zhang, Guoguo Chen, Kai Yu, Xie Chen