Automatic speech recognition for Algerian social-media speech is hard because of dialectal variation, Arabic-French code-switching, and non-standard orthography, yet these difficulties are usually summarised by a single word error rate that hides where recognition fails and whether gains reach information-bearing content. We evaluate seven systems on roughly 30 minutes of Algerian political social-media speech, annotated by language variety as Algerian dialect, Modern Standard Arabic, code-switched, or French: a Whisper-large-v3 baseline, five Whisper variants fine-tuned on different combinations of broadcast, dialectal, and code-switched speech, and the prompted Qwen3-Omni model. The best system reduces overall WER from 60.1% to 31.3%, but still reaches 44.0% on code-switched speech, and named entities remain harder than ordinary tokens, with fewer than half preserved. These results show that ASR difficulty on Algerian social-media speech cannot be read from overall WER alone; it must be measured separately by language variety and by whether named entities survive.

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.