Papers by Arun Balaji Buduru
FAtNet: Cost-Effective Approach Towards Mitigating the Linguistic Bias in Speaker Verification Systems (2022.findings-naacl)
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| Challenge: | Linguistic bias in Deep Neural Network (DNN) based systems is a critical challenge that needs attention. |
| Approach: | They propose to integrate a lightweight embedding with existing NLP systems to mitigate linguistic bias without adaptation. |
| Outcome: | The proposed framework reduces linguistic bias and enhances usability of baselines for twelve languages. |
Indic-CodecFake meets SATYAM: Towards Detecting Neural Audio Codec Synthesized Speech Deepfakes in Indic Languages (2026.findings-acl)
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| Challenge: | Speech deepfakes are highly realistic and can generate a few seconds of recorded speech. |
| Approach: | They propose an ALM that integrates semantic and prosodic representations from Whisper and TRILLsson to generate a speech deepfake dataset. |
| Outcome: | The proposed framework outperforms existing ALMs on the ICF benchmark in Indic languages. |
Investigating Prosodic Signatures via Speech Pre-Trained Models for Audio Deepfake Source Attribution (2025.findings-acl)
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| Challenge: | x-vector (speaker recognition PTM) achieves the highest performance in prosodic tasks . despite its low parameter, x vector captures unique prosodic characteristics of the sources . |
| Approach: | They propose to use SOTA speech pre-trained models to capture prosodic sig-natures of generative sources for audio deepfake source attribution. |
| Outcome: | The proposed model captures prosodic sig-natures of generative sources better than other models on ASVSpoof and CFAD. |
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake (2024.findings-naacl)
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| Challenge: | a recent study has focused on audio deepfake detection (ADD) due to its ability to impersonate and share false, often malicious information. |
| Approach: | They propose to use multilingual speech Pre-Trained models for Audio deepfake detection (ADD) they propose to combine models with existing models to achieve better ADD detection . |
| Outcome: | The proposed models gain knowledge about diverse pitches, accents, and tones, during theirpre-training phase and are more robust to variations. |