Speech-data/Greek-Speech-Dataset
🎧 Greek Speech Dataset The Greek Speech Dataset is a structured and high-quality speech audio dataset designed to support modern AI systems that rely on diverse audio data and multilingual voice data. It contains 184 hours of recordings distributed across 592 files, stored in MP3 and WAV formats, with a total size of 330 MB. This carefully curated audio dataset delivers balanced representation across speakers, including 49% female and 51% male participants, and a broad age… See the full description on the dataset page: https://huggingface.co/datasets/Speech-data/Greek-Speech-Dataset.
🎧 Greek Speech Dataset
The Greek Speech Dataset is a structured and high-quality speech audio dataset designed to support modern AI systems that rely on diverse audio data and multilingual voice data. It contains 184 hours of recordings distributed across 592 files, stored in MP3 and WAV formats, with a total size of 330 MB. This carefully curated audio dataset delivers balanced representation across speakers, including 49% female and 51% male participants, and a broad age range from 18 to 50+ years. The dataset language is Greek, with contributions from speakers across Greece, Cyprus, the USA, Australia, Germany, and the UK, ensuring a globally representative language speech dataset.
🔗 Learn more: https://speech-data.ai/datasets/greek/
🚀 Use Cases
This Greek speech dataset is designed for advanced AI and NLP applications, including speech recognition, voice assistant development, and natural language processing. The structured speech data supports acoustic modeling, speaker identification, and robust AI training pipelines. It also enhances multilingual system development where reliable speech audio dataset resources are required. As a high-quality speech recognition dataset, it is suitable for both academic research and production-level deployment in voice technologies.
📊 Dataset Metadata
⭐ Key Value
The key strength of this voice dataset lies in its linguistic diversity, balanced demographic structure, and high-quality audio data suitable for real-world AI training. It improves model robustness across accents and regional variations, making it a valuable resource for scalable speech dataset development and multilingual speech technology systems.