The Greek Speech Dataset is a comprehensive collection of high-quality audio recordings featuring native Greek speakers from Greece, Cyprus, USA, Australia, Germany, and UK. This professionally curated dataset contains 184 hours of authentic Greek speech data, meticulously annotated and structured for machine learning applications. Designed for speech recognition, natural language processing, and AI training tasks, this dataset captures the linguistic diversity and phonetic nuances of Greek across different global diaspora communities.
With balanced representation across gender and age groups, the dataset provides researchers and developers with a robust foundation for building accurate Greek language models, voice assistants, and conversational AI systems that serve both Hellenic and international Greek-speaking populations. The audio files are delivered in MP3/WAV format with consistent quality standards, making them immediately ready for integration into your ML pipeline.
Dataset General Info
| Parameter | Details |
| Size | 184 hours |
| Format | MP3/WAV |
| Tasks | Speech recognition, AI training, voice assistant development, natural language processing, acoustic modeling, speaker identification |
| File size | 330 MB |
| Number of files | 592 files |
| Gender of speakers | Female: 49%, Male: 51% |
| Age of speakers | 18-30 years: 35%, 31-40 years: 22%, 40-50 years: 18%, 50+ years: 25% |
| Countries | Greece, Cyprus, USA, Australia, Germany, UK |
Use Cases
Voice Assistant Development: The Greek Speech Dataset enables developers to create sophisticated voice-activated assistants and smart home devices that understand Greek commands across different regional accents from Greece, Cyprus, and diaspora communities. The diverse speaker pool ensures robust performance for Greek-speaking users globally, whether in Athens, Nicosia, Melbourne, or New York.
Tourism and Hospitality Services: Hotels, airports, and travel companies can leverage this dataset to build automated customer support systems and multilingual information kiosks that serve Greek-speaking tourists. Interactive voice response solutions help international businesses communicate effectively with Greek clients, enhancing service quality in tourism-dependent economies.
Cultural Heritage Preservation: Museums, libraries, and cultural institutions can utilize this dataset for creating voice-guided tours, interactive exhibits, and digital archives that preserve Greek linguistic heritage. Educational applications support Greek language learning and transmission across diaspora communities, maintaining cultural connections for second and third-generation Greek speakers worldwide.
FAQ
Q: What is included in the Greek Speech Dataset?
A: The Greek Speech Dataset includes 184 hours of audio recordings from native Greek speakers across Greece, Cyprus, USA, Australia, Germany, and UK. The dataset contains 592 files in MP3/WAV format, totaling approximately 330 MB. Each recording is professionally annotated with transcriptions, speaker metadata including age, gender, and geographic origin, along with quality markers to ensure optimal performance for machine learning applications targeting Greek-speaking populations worldwide.
Q: How does the dataset handle Greek dialects and regional variations?
A: The dataset captures various Greek dialects and accents including Standard Modern Greek, Cypriot Greek, and Pontic Greek influences from diaspora communities. With speakers from six different countries and regions, the dataset provides comprehensive coverage of phonetic variations, ensuring trained models can understand Greek speakers regardless of their geographic background or regional accent patterns.
Q: What audio quality and format does the dataset provide?
A: The dataset is available in both MP3 and WAV formats to accommodate different use cases. WAV files provide lossless audio quality ideal for research and high-accuracy applications, while MP3 files offer compressed formats suitable for production environments with storage constraints. All recordings maintain consistent audio quality with clear speech capture and minimal background noise, recorded at professional standards.
Q: How diverse is the speaker representation in the dataset?
A: The dataset features balanced demographic representation with 49% female and 51% male speakers. Age distribution includes 35% speakers aged 18-30, 22% aged 31-40, 18% aged 40-50, and 25% aged 50+. Speakers represent Greek-speaking communities across multiple continents, ensuring comprehensive representation of the global Hellenic linguistic community.
Q: What machine learning tasks is this dataset suitable for?
A: The Greek Speech Dataset is designed for various ML applications including automatic speech recognition, speaker identification, voice authentication, sentiment analysis, natural language understanding, acoustic modeling, and conversational AI development. The professionally annotated transcriptions and diverse speaker pool make it ideal for training supervised learning models for Greek language technology.
Q: Is the dataset suitable for commercial applications?
A: Yes, the Greek Speech Dataset is licensed for both research and commercial use. It can be integrated into commercial products, voice assistants, customer service automation, mobile applications, and other business solutions serving Greek-speaking markets in Europe, North America, Australia, and globally.
Q: How is the data annotated and structured?
A: Each audio file includes detailed annotations with orthographic transcriptions in Greek script, speaker demographics, recording conditions, and quality metrics. The dataset is organized with clear file naming conventions and includes metadata files in standard formats like JSON and CSV for easy integration with popular ML frameworks including TensorFlow, PyTorch, and Kaldi.
Q: What technical support is available for dataset implementation?
A: Comprehensive documentation is provided including dataset structure guides, code examples for popular ML frameworks, preprocessing scripts, and best practices for model training with Greek language data. Technical support is available for integration assistance, troubleshooting, and guidance on optimizing models for Greek speech recognition tasks.
How to Use the Speech Dataset
Step 1: Dataset Acquisition
Download the dataset package from the provided link. Upon purchase, you will receive access credentials and download instructions via email. The dataset is delivered as a compressed archive file containing all audio files, transcriptions, and metadata.
Step 2: Extract and Organize
Extract the downloaded archive to your local storage or cloud environment. The dataset follows a structured folder organization with separate directories for audio files, transcriptions, metadata, and documentation. Review the README file for detailed information about file structure and naming conventions.
Step 3: Environment Setup
Install required dependencies for your chosen ML framework such as TensorFlow, PyTorch, Kaldi, or others. Ensure you have necessary audio processing libraries installed including librosa, soundfile, pydub, and scipy. Set up your Python environment with the provided requirements.txt file for seamless integration.
Step 4: Data Preprocessing
Load the audio files using the provided sample scripts. Apply necessary preprocessing steps such as resampling, normalization, and feature extraction including MFCCs, spectrograms, or mel-frequency features. Use the included metadata to filter and organize data based on speaker demographics, recording quality, or other criteria relevant to your application.
Step 5: Model Training
Split the dataset into training, validation, and test sets using the provided speaker-independent split recommendations to avoid data leakage. Configure your model architecture for the specific task whether speech recognition, speaker identification, or other applications. Train your model using the transcriptions and audio pairs, monitoring performance on the validation set.
Step 6: Evaluation and Fine-tuning
Evaluate model performance on the test set using standard metrics such as Word Error Rate for speech recognition or accuracy for classification tasks. Analyze errors and iterate on model architecture, hyperparameters, or preprocessing steps. Use the diverse speaker demographics to assess model fairness and performance across different groups.
Step 7: Deployment
Once satisfactory performance is achieved, export your trained model for deployment. Integrate the model into your application or service infrastructure. Continue monitoring real-world performance and use the dataset for ongoing model updates and improvements as needed.
For detailed code examples, integration guides, and troubleshooting tips, refer to the comprehensive documentation included with the dataset.
