The Dagbani Speech Dataset provides an extensive repository of authentic audio recordings from native Dagbani speakers across Ghana and North Togo. This specialized linguistic resource contains 196 hours of professionally recorded Dagbani speech, accurately annotated and organized for sophisticated machine learning tasks. As a prominent language of Northern Ghana, Dagbani is documented with its unique phonetic characteristics and tonal variations essential for building effective speech recognition and language processing systems.
The dataset features balanced demographic distribution across gender and age categories, offering comprehensive representation of Dagbani’s linguistic diversity. Available in MP3/WAV format with consistent audio quality, this dataset is specifically designed for AI researchers, speech technologists, and developers creating voice applications, conversational AI, and natural language understanding systems for West African languages.
Dataset General Info
| Parameter | Details |
| Size | 196 hours |
| Format | MP3/WAV |
| Tasks | Speech recognition, AI training, voice assistant development, natural language processing, acoustic modeling, speaker identification |
| File size | 191 MB |
| Number of files | 778 files |
| Gender of speakers | Female: 48%, Male: 52% |
| Age of speakers | 18-30 years: 31%, 31-40 years: 28%, 40-50 years: 19%, 50+ years: 22% |
| Countries | Ghana, North Togo |
Use Cases
Government Services: Public sector organizations can employ the Dagbani Speech Dataset to build voice-enabled citizen service platforms, emergency response systems, and information hotlines that communicate effectively with Dagbani-speaking communities in Northern Ghana, improving public service delivery.
Mobile Communication: Telecommunications companies can leverage this dataset to develop speech-to-text messaging services, voice dialing systems, and interactive voice menus that enhance mobile phone accessibility for Dagbani speakers, particularly in areas with lower literacy rates.
E-Commerce Solutions: Online retailers and marketplace platforms can utilize this dataset to create voice-based shopping assistants, product search interfaces, and customer support systems that enable Dagbani speakers to access digital commerce platforms more easily, expanding market reach.
FAQ
Q: What is included in the Dagbani Speech Dataset?
A: The Dagbani Speech Dataset features 196 hours of professionally recorded audio from native Dagbani speakers in Ghana and North Togo. The collection comprises 778 annotated files in MP3/WAV format (approximately 191 MB), complete with orthographic transcriptions, tonal markings, speaker demographics, and linguistic annotations for comprehensive ML training.
Q: Why is Dagbani speech data important for language technology?
A: Dagbani is a major language of Northern Ghana with over 2 million speakers, yet it remains underrepresented in language technology. This dataset addresses the digital language divide by providing essential resources for developing speech recognition, translation, and voice interface technologies that serve Dagbani-speaking communities in education, healthcare, governance, and commerce.
Q: How does the dataset account for Dagbani’s linguistic features?
A: Dagbani has distinctive phonological characteristics including tone, vowel length distinctions, and nasal consonants. The dataset includes detailed linguistic annotations marking these features, ensuring trained models can accurately recognize and process Dagbani’s unique sound patterns and prosodic features.
Q: What is the demographic breakdown of speakers?
A: The dataset includes balanced representation with 48% female and 52% male speakers. Age distribution spans 31% (18-30 years), 28% (31-40), 19% (40-50), and 22% (50+), ensuring models trained on this data perform well across demographic groups.
Q: Can this dataset support multilingual or code-switching research?
A: While primarily focused on Dagbani, the dataset’s regional context captures natural code-switching patterns common in Northern Ghana. This makes it valuable for research on multilingual speech processing, language contact phenomena, and developing systems that handle mixed-language input typical of real-world usage.
Q: What quality control measures were applied?
A: Each recording underwent multiple quality checks including audio clarity assessment, transcription accuracy verification, annotation consistency review, and metadata validation. Only recordings meeting strict quality thresholds were included, ensuring the dataset provides reliable, high-quality data for ML applications.
Q: How should researchers cite or acknowledge this dataset?
A: Users should provide appropriate attribution in publications, products, or services that utilize the Dagbani Speech Dataset. Specific citation formats and acknowledgment guidelines are included in the dataset documentation, supporting academic research while respecting contributor rights and intellectual property.
Q: What makes this dataset suitable for production deployments?
A: With 196 hours of diverse, quality-controlled speech data, consistent formatting, comprehensive annotations, and balanced speaker representation, the dataset provides production-ready resources for building reliable Dagbani language applications. The professional recording quality and detailed documentation enable rapid development and deployment of speech-enabled services.
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 (TensorFlow, PyTorch, Kaldi, or others). Ensure you have necessary audio processing libraries installed (librosa, soundfile, pydub). 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 (e.g., MFCCs, spectrograms). 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 (ASR, speaker recognition, etc.). 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 (WER for speech recognition, 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.
For detailed code examples, integration guides, and troubleshooting tips, refer to the comprehensive documentation included with the dataset.
