CDAC - SVNIT submission for IWSLT 2025 I ndic track shared task (2025)

Abstract: In this paper, we designed a Speech-to-Text Translation (ST) system to translate English into Hindi, Bengali, and Tamil, and vice versa. We explored both cascaded and End-to-End (E2E) approaches as part of the IWSLT 2025 Indic shared task.

## introduction in a global and borderless economy, seamless communication is essential, with speech being the most natural medium. overcoming language barriers through intelligent systems is crucial for real-time interaction and bridging the digital divide @xcite . speech-to-text translation has a vital role to play in facilitating communication across language barriers. recent advancements in the area of speech technology have resulted in stateof-the-art performance in the speech recognition task @xcite and machine translation @xcite @xcite for almost all major languages. this encourages the advent of direct speech translation of speech, leading to the rise of two different paradigms of achieving the same. they are: cascaded and end-to-end speech translation. in the cascaded speech-to-text (st) translation paradigm, the task of translating speech from a source language to text in a target language is broken down into two distinct modules. the recent rise of cascaded st systems @xcite @xcite for translating the educational content in indian languages show the effectiveness of this approach. ## data in the iwslt 2025 indic speech translation shared task @xcite , multilingual speech translation dataset spanning six distinct language pairs involving english and three indic languages are released. ## experimental results for the iwslt 2025 indic speech translation shared task @xcite , we participate in two different settings: a) unconstrained cascaded and b) constrained end-to-end speechtranslation track. the experiments are not multilingual, but individual language-pairs are trained separately. we conduct experiments on all six language pairs: english to hindi (en-hi), bengali (en-bn), tamil (en-ta), and the reverse directions hi-en, bn-en, ta-en respectively. the results of our experiments are presented in english-to-indic (en-hi, en-bn, en-ta): the cascaded system consistently outperform the e2e system across all the language pairs. for example, en-hi achieves a bleu of 44. @xcite and chrf++ of 64.17 in the cascaded setup, compared to 34.61 bleu and 54.48 chrf++ in the e2e setup. similarly, the en-bn model scores 36.95 bleu (chrf++: 65.21) in the cascaded mode versus 31.56 bleu (chrf++: 58.22) in e2e. the trend continues with en-ta, where the bleu drops from 29.34 (chrf++: 66.15) in cascaded to 21.34 (chrf++: 56.07) in the e2e. these results indicate that the cascaded approach remains advantageous for english-to-indic translation, likely due to the mature asr performance on english audio and the robustness of the indictrans2 nmt system trained on diverse high-quality parallel corpora. the modular nature of the pipeline allows each component to be fine-tuned independently, maximizing their respective capabilities. indic-to-english (hi-en, bn-en, ta-en): the performance gap between cascaded and e2e systems is more pronounced in the indic-to-english direction. for hi-en, the cascaded system achieves 41.04 bleu and 67.05 chrf++, compared to 15.41 bleu and 42.96 chrf++ in the e2e track. for bn-en, the e2e model performs poorly, with only 0.459 bleu and 14.30 chrf++, while the cascaded model reaches 14.77 bleu and 44.88 chrf++. preliminary analysis suggests that smaller amount of training data and excessive use of codemixed language in the test set are the reason for low score for the bengali-english pair. similarly, the bleu score of ta-en model drops from 15.70 in the cascaded setup to 5.04 in the e2e setup. the sharp decline in the e2e performance for indic-to-english suggests that asr on indic audio remains a major challenge, especially in the constrained setup where access to external data or pre-trained language models is restricted. the e2e system must learn both transcription and translation jointly, which becomes challenging in lowresource settings or in speech settings consisting of code-mixed, noisy, or accented content. this highlights the difficulty of training e2e models for the indic-origin speech, where the diversity in speech patterns and lack of rich supervised training data severely affect generalization. ## conclusion our experiments show that cascaded models still hold a strong edge in terms of accuracy and robustness, particularly in indic to en settings while end-to-end speech translation models can be an alternative due to their simplicity and integration. with further work of using transfer learning from larger models, multilingual pre-training and data augmentation techniques such as use of synthetic data, e2e models can be at par with the cascaded models by overcoming low-resource bottlenecks in indic languages.