Multi-language Speech-to-Text Translation (ST) plays a crucial role in breaking linguistic barriers, particularly in multilingual regions like India. This paper focuses on building a robust ST system for low resource Indian languages, with a special emphasis on Bengali and Tamil. These languages represent the Indo-Aryan and Dravidian families, respectively. The dataset used in this work comprises spoken content from TED Talks and conferences, paired with transcriptions in English and their translations in Bengali and Tamil. Our work specifically addresses the translation of Bengali and Tamil speech to English text, a critical area given the scarcity of annotated speech data. To enhance translation quality and model robustness, we leverage cross-lingual resources and word level translation strategies. The ultimate goal is to develop an end-to-end ST model capable of real-world deployment for under represented languages.

Speech recognition has received extensive research attention in recent years. It becomes much more challenging when the speaker’s age, gender and other factors introduce variations in the speech. In this work, we propose a fine-tuned automatic speech recognition model derived from OpenAI’s whisperlarge-v2. Though we experimented with both Whisper-large and Wav2vec2-XLSR-large, the reduced WER of whisper-large proved to be a superior model. We secured 4th rank in the LT-EDI-2025 shared task. Our implementation details and code are available at our GitHub repository1.