Automatic Speech Recognition (ASR) has significantly transformed human-computer-interaction and natural language processing. However, many African spoken languages, including Efik, remain severely underrepresented in ASR research. This paper investigates the adoption of state-of-the-art foundational ASR models such as XLS-R and Whisper through fine-tuning for Efik, a low-resource tonal language and empirically evaluates their performance. We curate a 3-hour Efik speech dataset and conduct a comparative evaluation using standard ASR metrics. We further augmented the XLS-R CTC model with a 3-gram KenLM language model trained on an Efik text corpus. Experimental results show that XLS-R-300M + KenLM achieves a word error rate (WER) of 10.86% and a character error rate (CER) of 3.16%, substantially outperforming both the baseline XLS-R (WER: 29.2%, CER: 6.4%) and Whisper across noisy and multi-speaker conditions. These findings suggest that lightweight CTC models augmented with language model integration offer a more robust and practical approach for extremely low-resource tonal languages than larger sequence-to-sequence models.

Low-resource languages serve as invaluable repositories of human history, preserving cultural and intellectual diversity. Despite their significance, they remain largely absent from modern natural language processing systems. While progress has been made for widely spoken African languages such as Swahili, Yoruba, and Amharic, smaller indigenous languages like Efik continue to be underrepresented in machine translation research. This study evaluates the effectiveness of state-of-the-art multilingual neural machine translation models for English–Efik translation, leveraging a small-scale, community-curated parallel corpus of N = 13,865 sentence pairs. We fine-tuned both the mT5 multilingual model and the NLLB-200 model on this dataset. NLLB-200 outperformed mT5, achieving BLEU scores of 26.64 for English–Efik and 31.21 for Efik–English, with corresponding chrF scores of 51.04 and 47.92, indicating improved fluency and semantic fidelity. Our findings demonstrate the feasibility of developing practical machine translation tools for low-resource languages and highlight the importance of inclusive data practices and culturally grounded evaluation in advancing equitable NLP.