The Catalan Speech Dataset is a meticulously curated collection of high-quality audio recordings from native Catalan speakers across Catalonia, Valencia, Balearic Islands, Andorra, France, and Italy. This comprehensive linguistic resource features 140 hours of authentic Catalan speech data professionally annotated and structured for advanced machine learning applications.
Catalan, spoken by over 10 million people with official status in Andorra and regional status in Spain, is captured with distinctive Romance language phonological features crucial for developing accurate speech recognition technologies.
Dataset General Info
| Parameter | Details |
| Size | 140 hours |
| Format | MP3/WAV |
| Tasks | Speech recognition, AI training, voice assistant development, natural language processing, acoustic modeling, speaker identification |
| File size | 245 MB |
| Number of files | 650 files |
| Gender of speakers | Female: 47%, Male: 53% |
| Age of speakers | 18-30 years: 27%, 31-40 years: 25%, 40-50 years: 24%, 50+ years: 24% |
| Countries | Spain (Catalonia, Valencia, Balearic Islands), Andorra, France, Italy (Alghero) |
Use Cases
Regional Identity and Autonomy: Catalan government agencies can utilize the Catalan Speech Dataset to develop voice-enabled regional services, digital administration platforms, and citizen engagement systems supporting Catalan linguistic sovereignty. Voice interfaces implement Catalan language rights, support regional identity in Catalonia and Valencia, enable digital services in co-official language, and facilitate self-governance through appropriate linguistic technology. Applications include Generalitat de Catalunya e-services, municipal platforms, healthcare systems, education administration, and regional information portals.
Cultural Heritage and Language Preservation: Cultural organizations across Catalan-speaking territories can leverage this dataset to build language preservation platforms, cultural documentation systems, and heritage conservation tools. Voice technology preserves Catalan linguistic heritage threatened by language shift, supports language normalization efforts, enables cultural transmission to younger generations, and maintains Catalan identity through digital innovation. Applications include cultural heritage platforms, literary archives including works of Mercè Rodoreda, traditional music preservation, oral history projects, and educational resources promoting Catalan language vitality.
Tourism and Economic Development: Tourism operators in Catalonia, Valencia, Balearic Islands, and Andorra can employ this dataset to create voice-guided tours, multilingual hospitality services, and tourism information platforms in Catalan. Voice technology enhances visitor experiences at Sagrada Família, Park Güell, and Mediterranean destinations while promoting Catalan language, supports regional tourism economies, enables authentic cultural experiences, and creates differentiated tourism products. Applications include heritage site guides, hotel service interfaces, restaurant recommendation systems, and tourism platforms celebrating Catalan Modernisme architecture and Mediterranean culture.
FAQ
Q: What is included in this dataset?
A: The dataset includes 140 hours of audio recordings with 650 files totaling 245 MB, complete with transcriptions and linguistic annotations.
Q: How diverse is the speaker demographic?
A: Features 47% female and 53% male speakers across age groups: 27% (18-30), 25% (31-40), 24% (40-50), 24% (50+).
How to Use the Speech Dataset
Step 1: Dataset Acquisition – Download the dataset package from the provided link upon purchase.
Step 2: Extract and Organize – Extract to your storage and review the structured folder organization.
Step 3: Environment Setup – Install ML framework dependencies and audio processing libraries.
Step 4: Data Preprocessing – Load audio files and apply preprocessing steps like resampling and feature extraction.
Step 5: Model Training – Split into training/validation/test sets and train your model.
Step 6: Evaluation and Fine-tuning – Evaluate performance and iterate on architecture.
Step 7: Deployment – Export and integrate your trained model into production systems.
