Last modified: 7 hours ago
Project Overview KACCP is a specialized voice data collection platform designed to gather, process, and structure high-quality speech datasets for West African languages. Its primary function is to enable the creation of reliable, annotated audio data that can be used to train and improve speech-based AI systems such as Text-to-Speech (TTS) and Automatic Speech Recognition (ASR). The platform provides a simple interface for native speakers to record speech in their local languages, while internally managing data validation, annotation, and formatting to ensure the output is suitable for machine learning workflows. This transforms raw voice input into structured datasets ready for AI model training. KACCP focuses on languages that are currently underrepresented in global AI systems. By enabling scalable and community-driven data collection, it addresses the lack of accessible, high-quality speech data required to build voice technologies for these languages. The system is designed to support multiple languages and dialects, allowing for expansion across different regions. It incorporates mechanisms for maintaining data quality, including guided recording prompts, consistency checks, and annotation pipelines. Overall, KACCP serves as a foundational infrastructure layer for building voice-enabled technologies in low-resource language environments, turning everyday speech contributions into usable datasets for AI development.