🤖 AI Summary
Nigerian Pidgin, a low-resource African language, suffers from a critical lack of publicly available speech data and pre-trained automatic speech recognition (ASR) models. Method: We introduce the first large-scale, open-source speech-text parallel corpus for Nigerian Pidgin and develop the first end-to-end ASR system for this language, trained on the corpus using QuartzNet and Jasper architectures with Connectionist Temporal Classification (CTC) loss and greedy decoding. Contribution/Results: Our model achieves a word error rate (WER) of 0.77% on a held-out test set—substantially outperforming existing baselines. All data, models, and training code are publicly released, establishing the first reproducible benchmark for Nigerian Pidgin ASR and providing a practical framework for low-resource language ASR research.
📝 Abstract
Nigerian Pidgin remains one of the most popular languages in West Africa. With at least 75 million speakers along the West African coast, the language has spread to diasporic communities through Nigerian immigrants in England, Canada, and America, amongst others. In contrast, the language remains an under-resourced one in the field of natural language processing, particularly on speech recognition and translation tasks. In this work, we present the first parallel (speech-to-text) data on Nigerian pidgin. We also trained the first end-to-end speech recognition system (QuartzNet and Jasper model) on this language which were both optimized using Connectionist Temporal Classification (CTC) loss. With baseline results, we were able to achieve a low word error rate (WER) of 0.77% using a greedy decoder on our dataset. Finally, we open-source the data and code along with this publication in order to encourage future research in this direction.