This paper presents the submission of the Jadavpur University Computer Science and Engineering Natural Language Processing (JU-CSENLP) 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 low resource 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)-200-distilled-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.

We propose a compact hybrid quantum–classical extension of OpenAI’s Whisper in which classical components are replaced by Quantum Convolutional Neural Networks (QCNN), Quantum LSTMs (QLSTM), and optional Quantum Adaptive Self-Attention (QASA). Log-mel spectrograms are angle encoded and processed by QCNN kernels, whose outputs feed a Transformer encoder, while QLSTM-based decoding introduces quantum-enhanced temporal modeling. The design incorporates pretrained acoustic embeddings and is constrained to NISQ-feasible circuit depths and qubit counts. Although this work is primarily architectural, we provide a fully specified, reproducible evaluation plan using Speech Commands, LibriSpeech, and Common Voice, along with strong classical baselines and measurable hypotheses for assessing noise robustness, efficiency, and parameter sparsity. To our knowledge, this is the first hardware-aware, module-wise quantum replacement framework for Whisper.