Papers by Ranjeet Gupta
FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Models (2025.emnlp-industry)
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Karan Dua, Hitesh Laxmichand Patel, Puneet Mittal, Ranjeet Gupta, Amit Agarwal, Praneet Pabolu, Srikant Panda, Hansa Meghwani, Graham Horwood, Fahad Shah
| Challenge: | Document understanding models require large, diverse, and well-annotated datasets that can cost millions of dollars to collect and maintain. |
| Approach: | They propose a scalable synthetic data generation framework that combines Stochastic Schemas and Parameterized Sampling to produce realistic, multilingual semi-structured documents with rich annotations. |
| Outcome: | Experiments on key information extraction tasks show that the proposed framework improves the absolute F1 score by up to 11% while reducing annotation effort by over 90% compared to traditional hard-template methods. |
SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models (2025.acl-industry)
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| Challenge: | Text-to-Speech (TTS) training requires extensive and diverse text and speech data. |
| Approach: | They propose a synthetic speech data generation pipeline that generates multilingual, domain-specific datasets for TTS training. |
| Outcome: | The proposed pipeline generates data that is 10–48% more diverse than baseline across various linguistic and phonetic metrics, along with speaker-standardized speech audio while generating approximately 97% correctly normalized text. |