The Gheg Albanian Speech Dataset is a specialized collection of high-quality audio recordings capturing the Gheg dialect, the northern variety of Albanian spoken across Albania, Kosovo, North Macedonia, and Montenegro. This meticulously curated dataset is designed for machine learning researchers, linguists, and AI developers working on speech recognition, natural language processing, and voice technology applications.

Featuring native speakers from diverse regions and backgrounds, the dataset provides authentic representations of Gheg pronunciation, intonation, and linguistic features. Each recording is professionally annotated and available in both MP3 and WAV formats, ensuring compatibility with modern ML frameworks.

With balanced demographic representation across age groups and genders, this dataset offers comprehensive linguistic coverage essential for building accurate speech recognition systems and preserving this important dialectal variant of Albanian language for technological applications.

Gheg Albanian Dataset General Info

Field	Details
Size	183 hours
Format	MP3/WAV
Tasks	Speech recognition, dialect identification, AI training, acoustic modeling, phonetic research, voice assistant development
File Size	401 MB
Number of Files	847 files
Gender of Speakers	Male: 48%, Female: 52%
Age of Speakers	18-30 years old: 34%, 31-40 years old: 31%, 41-50 years old: 23%, 50+ years old: 12%
Countries	Albania (northern regions), Kosovo, North Macedonia, Montenegro

Use Cases

Dialect-Specific Speech Recognition: This dataset enables the development of speech recognition systems specifically tuned to Gheg Albanian, which differs significantly from Tosk Albanian in pronunciation and vocabulary. Applications include transcription services, voice-to-text systems, and accessibility tools for the millions of Gheg speakers across the Balkans.

Cross-Border Communication Applications: Companies and organizations operating across Albania, Kosovo, North Macedonia, and Montenegro can use this dataset to build unified voice platforms that understand Gheg speakers from different regions, facilitating seamless communication and customer service in telecommunications, banking, and e-commerce sectors.

Linguistic Preservation and Research: Academics and cultural institutions can leverage this dataset for documenting Gheg phonology, studying dialectal variations, and creating digital language archives. This supports efforts to preserve linguistic heritage and enables comparative studies between Albanian dialects and related languages in the Balkans.

FAQ

Q: What is Gheg Albanian and how does it differ from standard Albanian?

A: Gheg is the northern dialect of Albanian, spoken in northern Albania, Kosovo, North Macedonia, and Montenegro. It differs from Tosk (southern dialect and basis of standard Albanian) in phonology, vocabulary, and grammar. This dataset captures authentic Gheg speech patterns, making it essential for applications serving northern Albanian-speaking populations.

Q: Why do I need a Gheg-specific dataset instead of a general Albanian dataset?

A: Speech recognition systems trained on standard Albanian (based on Tosk) often perform poorly on Gheg speakers due to significant pronunciation and vocabulary differences. This Gheg-specific dataset ensures your models accurately recognize and process speech from the millions of Gheg speakers across multiple Balkans countries.

Q: Which regions and countries are represented in this dataset?

A: The dataset includes speakers from northern Albania, Kosovo, North Macedonia, and Montenegro, providing comprehensive coverage of Gheg-speaking regions and capturing regional variations within the dialect itself.

Q: How is the dataset balanced in terms of demographics?

A: The dataset features excellent gender balance (Male: 48%, Female: 52%) and diverse age representation (18-30: 34%, 31-40: 31%, 41-50: 23%, 50+: 12%), ensuring your ML models perform well across different demographic groups.

Q: What applications can benefit from this dataset?

A: Applications include voice assistants for Gheg speakers, call center automation, mobile apps for the Balkans market, educational language tools, healthcare communication systems, government services, media transcription, and any voice-enabled technology targeting Gheg Albanian-speaking populations.

Q: What is the technical quality of the recordings?

A: All recordings are captured using professional equipment in controlled environments, available in both MP3 and WAV formats. Each file undergoes quality control to ensure clear audio, appropriate volume levels, and minimal background noise suitable for ML training.

Q: How much training data is provided?

A: The dataset contains 183 hours of Gheg Albanian speech across 847 audio files, providing substantial data for training robust and accurate speech recognition models and other voice-based AI applications.

Q: Can this dataset be used for commercial products?

A: Yes, the Gheg Albanian Speech Dataset is licensed for both research and commercial use, allowing integration into products and services targeting Gheg-speaking markets across the Balkans region.

How to Use the Speech Dataset

Step 1: Dataset Acquisition

Purchase or request access to the Gheg Albanian Speech Dataset. After approval, you’ll receive secure download links for the complete package including audio files, transcriptions, metadata, and documentation. Choose between MP3 (compressed) or WAV (uncompressed) format based on your storage and quality requirements.

Step 2: Initial Setup

Download and extract the dataset to your working directory. Review the included documentation which provides detailed information about file structure, naming conventions, speaker demographics, transcription guidelines, and regional distribution of speakers.

Step 3: Environment Configuration

Prepare your machine learning environment with necessary tools and libraries. Install Python (3.7+), deep learning frameworks (TensorFlow, PyTorch, or Keras), and audio processing libraries (Librosa, SoundFile, pydub). Ensure sufficient storage space (minimum 2GB) and computing resources (GPU recommended for faster training).

Step 4: Data Exploration

Examine sample audio files to understand recording quality, speaker diversity, and speech characteristics. Review transcriptions for accuracy and formatting. Analyze metadata to understand demographic distribution and regional representation within the dataset.

Step 5: Preprocessing Pipeline

Implement preprocessing steps including audio normalization, noise reduction, silence removal, and segmentation. Extract relevant acoustic features such as MFCCs, mel-spectrograms, or raw waveforms depending on your model architecture. Apply any necessary language-specific preprocessing for Gheg Albanian phonetics.

Step 6: Data Splitting

Divide the dataset into training, validation, and test sets using appropriate ratios (e.g., 80-10-10 or 70-15-15). Ensure stratified splitting to maintain demographic balance across all sets. Consider speaker-independent splits to prevent overfitting on specific voices.

Step 7: Data Augmentation

Enhance model robustness by applying data augmentation techniques such as speed perturbation, pitch shifting, adding background noise, or time masking. This increases effective dataset size and helps models generalize better to real-world conditions.

Step 8: Model Development

Select and implement appropriate model architectures for your task (e.g., DeepSpeech, Wav2Vec, Whisper fine-tuning, or custom architectures). Configure hyperparameters, loss functions, and optimization algorithms suitable for Gheg Albanian speech recognition.

Step 9: Training and Monitoring

Train your model while monitoring key metrics including loss, accuracy, Word Error Rate (WER), and Character Error Rate (CER). Implement learning rate scheduling, early stopping, and checkpoint saving to optimize training efficiency and prevent overfitting.

Step 10: Evaluation and Testing

Thoroughly evaluate model performance on the test set using standard speech recognition metrics. Analyze errors by demographic groups, regional variations, and phonetic patterns to identify areas for improvement. Compare results with baseline models to measure advancement.

Step 11: Fine-tuning and Optimization

Based on evaluation results, refine your model through hyperparameter tuning, architecture modifications, or additional training. Consider ensemble methods or transfer learning from larger multilingual models for enhanced performance.

Step 12: Production Deployment

Deploy your trained model to production environments. This may include building REST APIs, integrating with mobile or web applications, or embedding in edge devices. Implement proper error handling, logging, and monitoring to ensure reliable performance for Gheg Albanian speakers.