Abstract
Ramsa is a developing 41-hour speech corpus of Emirati Arabic designed to support sociolinguistic research and low-resource language technologies. It contains recordings from structured interviews with native speakers and episodes from national television shows. The corpus features 157 speakers (59 female, 98 male), spans subdialects such as Urban, Bedouin, and Mountain/Shihhi, and covers topics such as cultural heritage, agriculture and sustainability, daily life, professional trajectories, and architecture. It consists of 91 monologic and 79 dialogic recordings, varying in length and recording conditions. A 10\% subset was used to evaluate commercial and open-source models for automatic speech recognition (ASR) and text-to-speech (TTS) in a zero-shot setting to establish initial baselines. Whisper-large-v3-turbo achieved the best ASR performance, with average word and character error rates of 0.268 and 0.144, respectively. MMS-TTS-Ara reported the best mean word and character rates of 0.285 and 0.081, respectively, for TTS. These baselines are competitive but leave substantial room for improvement. The paper highlights the challenges encountered and provides directions for future work.
Abstract (translated)
Ramsa 是一个正在开发中的41小时的阿联酋阿拉伯语语音语料库，旨在支持社会语言学研究和低资源语言技术的发展。该语料库包含与本地母语者的结构化访谈录音以及国家电视台节目的片段。它包括了来自157位说话者（其中女性59人，男性98人）的录音，涵盖了都市、贝都因人和山区/希赫方言等子方言，并涉及文化传统、农业及可持续性、日常生活、职业路径和建筑等话题。该语料库包含91段独白录音和79段对话录音，时长和录音条件各不相同。 为了在零样本设置下评估商业和开源模型的自动语音识别（ASR）和文本到语音（TTS）性能，并建立初步基准线，使用了语料库中10%的数据集进行了测试。Whisper-large-v3-turbo 在 ASR 方面表现最佳，其平均词错误率（WER）为0.268，字符错误率（CER）为0.144；而 MMS-TTS-Ara 报告了 TTS 的最佳均值词和字符速率，分别为 0.285 和 0.081。这些基准虽然具备竞争力，但仍存在很大的改进空间。论文还指出了所遇到的挑战，并为未来的工作提供了方向。
URL
https://arxiv.org/abs/2603.08125
