JU-CSE-NLP’s Cascaded Speech to Text Translation Systems for IWSLT 2025 in Indic Track

This paper presents the submission of the Jadavpur University Computer Science and Engineering Natural Language Processing (JU-CSE-NLP) Laboratory to the International Conference on Spoken Language Translation (IWSLT) 2025 Indic track, addressing the speech-to-text translation task in both English-to-Indic (Bengali, Hindi, Tamil) and Indic-to-English directions.To tackle the challenges posed by lowresource Indian languages, we adopt a cascaded approach leveraging state-of-the-art pre-trained models.For English-to-Indic translation, we utilize OpenAI's Whisper model for Automatic Speech Recognition (ASR), followed by the Meta's No Language Left Behind (NLLB)-200distilled-600M model finetuned for Machine Translation (MT).For the reverse direction, we employ the AI4Bharat's IndicConformer model for ASR and IndicTrans2 finetuned for MT.Our models are fine-tuned on the provided benchmark dataset to better handle the linguistic diversity and domain-specific variations inherent in the data.Evaluation results demonstrate that our cascaded systems achieve competitive performance, with notable BLEU and chrF++ scores across all language pairs.Our findings highlight the effectiveness of combining robust ASR and MT components in a cascaded pipeline, particularly for low-resource and morphologically rich Indian languages.