This paper introduced FFSTC 2, an expanded version of the existing Fongbe-to-French speech translation corpus, addressing the critical need for resources in African dialects for speech recognition and translation tasks. We extended the dataset by adding 36 hours of transcribed audio, bringing the total to 61 hours, thereby enhancing its utility for both automatic speech recognition (ASR) and speech translation (ST) in Fongbe, a low-resource language. Using this enriched corpus, we developed both cascade and end-to-end speech translation systems. Our models employ AfriHuBERT and HuBERT147, two speech encoders specialized to African languages, and the NLLB and mBART models as decoders. We also investigate the use of the SAMU-XLSR approach to inject sentence-level semantic information to the XSLR-128 model used as an alternative speech encoder. We also introduced a novel diacritic-substitution technique for ASR, which, when combined with NLLB, enables a cascade model to achieve a BLEU score of 37.23 ompared to 39.60 obtained by the best system using original diacritics. Among the end-to-end architectures evaluated, the architectures with data augmentation and NLLB as decoder achieved the highest score respectively, SAMU-NLLB scored the BLEU score of 28.43.

In this paper, we present the approach and system setup of our participation in the IWSLT 2025 low-resource speech translation shared task. We submitted systems for three language pairs, namely Tunisian Arabic to English, North Levantine Arabic to English, and Fongbé to French. Both pipeline and end-to-end speech translation systems were explored for Tunisian Arabic to English and Fongbé to French pairs. However, only pipeline approaches were investigated for the North Levantine Arabic–English translation direction. All our submissions are based on the usage of pre-trained models that we further fine-tune with the shared task training data.

In this paper, we introduce the Fongbe to French Speech Translation Corpus (FFSTC). This corpus encompasses approximately 31 hours of collected Fongbe language content, featuring both French transcriptions and corresponding Fongbe voice recordings. FFSTC represents a comprehensive dataset compiled through various collection methods and the efforts of dedicated individuals. Furthermore, we conduct baseline experiments using Fairseq’s transformer_s and conformer models to evaluate data quality and validity. Our results indicate a score BLEU of 8.96 for the transformer_s model and 8.14 for the conformer model, establishing a baseline for the FFSTC corpus.