Speech recognition system developed for low-resource Basaà language
Automatic Speech Recognition for the Basaà Language: A Low-Resource Approach
Computation and LanguageArtificial Intelligence
Summary
Many languages around the world lack good technology to turn speech into text because they don’t have enough data to train modern computer systems. The authors focus on Basaà, a language with limited resources, trying to create an automatic speech recognition (ASR) system despite these challenges. They discuss how big advances in AI have mostly helped popular languages but not smaller ones like Basaà. Their work aims to close that gap by using techniques suited for low-resource settings.
What this means in practice
- •For mobile app developers: Build Basaà speech-to-text features in mobile apps where existing tools don’t support this language.
- •For voice assistant designers: Create voice-activated assistants that understand and transcribe Basaà speech in low-data scenarios.
Authors
Sophie Gertrude Ngo Mock, Charles Moudina Varmantchaonala, Paul Dayang, Jean Michel Nlong, Christopher Gies
Abstract
The rapid advancement of Artificial Intelligence (AI) and Natural Language Processing (NLP) has revolutionized the way humans interact with machines. Among the most impactful developments is Automatic Speech Recognition (ASR), which enables computers to convert spoken language into text. Systems such as those built on deep neural networks, transformer architectures, and self-supervised learning have achieved near-human performance for well-resourced languages such as English and French. Yet, these advances have disproportionately benefited a small fraction of the world's languages.