BhasaAnuvaad: A Speech Translation Dataset for 14 Indian Languages

• URL: http://arxiv.org/abs/2411.04699v1
• Date: Thu, 07 Nov 2024 13:33:34 GMT
• Title: BhasaAnuvaad: A Speech Translation Dataset for 14 Indian Languages
• Authors: Sparsh Jain, Ashwin Sankar, Devilal Choudhary, Dhairya Suman, Nikhil Narasimhan, Mohammed Safi Ur Rahman Khan, Anoop Kunchukuttan, Mitesh M Khapra, Raj Dabre,
• Abstract summary: We evaluate the performance of widely-used Automatic Speech Translation systems on Indian languages. There is a striking absence of systems capable of accurately translating colloquial and informal language. We introduce BhasaAnuvaad, the largest publicly available dataset for AST involving 14 scheduled Indian languages.
• Score: 27.273651323572786
• License:
• Abstract: Automatic Speech Translation (AST) datasets for Indian languages remain critically scarce, with public resources covering fewer than 10 of the 22 official languages. This scarcity has resulted in AST systems for Indian languages lagging far behind those available for high-resource languages like English. In this paper, we first evaluate the performance of widely-used AST systems on Indian languages, identifying notable performance gaps and challenges. Our findings show that while these systems perform adequately on read speech, they struggle significantly with spontaneous speech, including disfluencies like pauses and hesitations. Additionally, there is a striking absence of systems capable of accurately translating colloquial and informal language, a key aspect of everyday communication. To this end, we introduce BhasaAnuvaad, the largest publicly available dataset for AST involving 14 scheduled Indian languages spanning over 44,400 hours and 17M text segments. BhasaAnuvaad contains data for English speech to Indic text, as well as Indic speech to English text. This dataset comprises three key categories: (1) Curated datasets from existing resources, (2) Large-scale web mining, and (3) Synthetic data generation. By offering this diverse and expansive dataset, we aim to bridge the resource gap and promote advancements in AST for low-resource Indian languages, especially in handling spontaneous and informal speech patterns.

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