1 citations · 1 across the 7 of their papers we have counts for
7 papers
TCG CREST System Description for the DISPLACE-M Challenge
Nikhil Raghav, Md Sahidullah
This report presents the TCG CREST system description for Track 1 (Speaker Diarization) of the DISPLACE-M challenge, focusing on naturalistic medical conversations in noisy rural-h…
MK-SGC-SC: Multiple Kernel Guided Sparse Graph Construction in Spectral Clustering for Unsupervised Speaker Diarization
Nikhil Raghav, Avisek Gupta, Swagatam Das +1
Speaker diarization aims to segment audio recordings into regions corresponding to individual speakers. Although unsupervised speaker diarization is inherently challenging, the pro…
The TCG CREST -- RKMVERI Submission for the NCIIPC Startup India AI Grand Challenge
Nikhil Raghav, Arnab Banerjee, Janojit Chakraborty +3
In this report, we summarize the integrated multilingual audio processing pipeline developed by our team for the inaugural NCIIPC Startup India AI GRAND CHALLENGE, addressing Probl…
Self-Tuning Spectral Clustering for Speaker Diarization
Nikhil Raghav, Avisek Gupta, Md Sahidullah +1
Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity matrix remains difficult due to the…
TCG CREST System Description for the Second DISPLACE Challenge
Nikhil Raghav, Subhajit Saha, Md Sahidullah +1
In this report, we describe the speaker diarization (SD) and language diarization (LD) systems developed by our team for the Second DISPLACE Challenge, 2024. Our contributions were…
Exploring Green AI for Audio Deepfake Detection
Subhajit Saha, Md Sahidullah, Swagatam Das
The state-of-the-art audio deepfake detectors leveraging deep neural networks exhibit impressive recognition performance. Nonetheless, this advantage is accompanied by a significan…
Assessing the Robustness of Spectral Clustering for Deep Speaker Diarization
Nikhil Raghav, Md Sahidullah
Clustering speaker embeddings is crucial in speaker diarization but hasn't received as much focus as other components. Moreover, the robustness of speaker diarization across variou…