JU - CSE - NLP ’s Cascaded Speech to Text Translation Systems for IWSLT 2025 in I ndic Track (2025)

Abstract: 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.

## introduction and related work speech to text translation (stt) has long been the interest of natural language processing (nlp) researchers particularly due to the huge number of languages spoken worldwide. however, a major part of research has been invested on translation between european languages and some asian languages such as chinese. thus, translation from english to indic languages and vice-versa is of considerable importance. this is further highlighted by the fact that indic languages like hindi, bengali and tamil have several speakers worldwide (615, 228 and 90.8 million respectively) @xcite . the top performers (nict @xcite and hwtsc @xcite ) of iwslt 2024 indic track competition showed the importance of finetuning asr and mt models in a cascaded system. we present in this paper, cascaded models for translation from english to indic languages @xcite and vice-versa. we also fine-tune our neural machine translation (nmt) models using iwslt 2025 indic track training dataset so as to obtain better results as compared to the pretrained checkpoints. the visual description of our system is shown in figure 1 . we prefer the cascaded system over the end to end (e2e) system for two reasons. firstly, most speech translators are not trained on indic speech and hence would require lot of resources to achieve the baseline performance that we have in cascaded systems. secondly even in the english to indic track, it has been shown in @xcite that cascaded models completely outperform their end to end counterparts. transcription and translation is carried out entirely on the basis of the given segmentation timestamps in the train, dev and test sets of the iwslt 2025 indic track. ## dataset description the dataset for each language pair was given in three parts for both train and development sets. these included the wav files containing the audio of the speaker, yaml files containing the audio metadata and the segmentation information and the text files containing the corresponding transcriptions and translations of the segments. the punctuations were present only in the translated text. it was observed that the source files in english to indic direction were same for the three languages. the test datasets contained only wav files and yaml files. ## methodology we have employed cascaded systems for both english to indic (hindi, bengali, tamil) as well as indic (hindi, bengali, tamil) to english translations. we describe the preprocessing, finetuning and inference procedure of our models in the subsequent subsections. ## experiments the fine-tuning was conducted on a multi-gpu setup using kaggle gpu t4x2 for efficient parallelprocessing. to optimize training, audio-related metadata were removed. ## results the final finetuned results on the dev set are given in bengali, hindi, and tamil using indicconformer were 48.32%, 36.93%, and 56.17%, respectively. @xcite the results on the test set as calculated by iwslt are given in ## limitation due to resource constraints, it was not feasible to fine-tune the asr models to reduce the word error rate, which directly affects the quality of input provided to the nmt system. additionally, the fine-tuning of the nmt models was limited to a maximum of five epochs, further constraining potential improvements in translation performance. ## conclusion and future work this paper presented ju-cse-nlp's submission to the indic track of iwslt 2025. we have highlighted the detailed methodology of our preprocessing, finetuning and inference procedures in our paper which will help further research and system development in the field of speech translation of indic languages. our results are also quite reasonable comparing with previous year's performance in the same track @xcite as shown in