Search Results for author:
Found 12 papers, 5 papers with code
Overlap-aware End-to-End Supervised Hierarchical Graph Clustering for Speaker Diarization
Speaker diarization, the task of segmenting an audio recording based on speaker identity, constitutes an important speech pre-processing step for several downstream applications.
Summary of the DISPLACE Challenge 2023 -- DIarization of SPeaker and LAnguage in Conversational Environments
In multi-lingual societies, where multiple languages are spoken in a small geographic vicinity, informal conversations often involve mix of languages.
DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments
The challenge attempts to highlight outstanding issues in speaker diarization (SD) in multilingual settings with code-mixing.
Audio Retrieval for Multimodal Design Documents: A New Dataset and Algorithms
We consider and propose a new problem of retrieving audio files relevant to multimodal design document inputs comprising both textual elements and visual imagery, e. g., birthday/greeting cards.
Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization
We also propose an approach to jointly update the embedding extractor and the GNN model to perform end-to-end speaker diarization (E2E-SHARC).
Self-Supervised Metric Learning With Graph Clustering For Speaker Diarization
In this paper, we propose an approach that jointly learns the speaker embeddings and the similarity metric using principles of self-supervised learning.
Self-supervised Representation Learning With Path Integral Clustering For Speaker Diarization
In this paper, we propose a representation learning and clustering algorithm that can be iteratively performed for improved speaker diarization.
LEAP Submission for the Third DIHARD Diarization Challenge
This paper describes the challenge submission, the post-evaluation analysis and improvements observed on the DIHARD-III dataset.
The Third DIHARD Diarization Challenge
DIHARD III was the third in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variability in recording equipment, noise conditions, and conversational domain.
Deep Self-Supervised Hierarchical Clustering for Speaker Diarization
In this paper, we propose a novel algorithm for hierarchical clustering which combines the speaker clustering along with a representation learning framework.
Audio and Speech Processing
LEAP System for SRE19 CTS Challenge -- Improvements and Error Analysis
In this paper, we provide a detailed account of the LEAP SRE system submitted to the CTS challenge focusing on the novel components in the back-end system modeling.
Pairwise Discriminative Neural PLDA for Speaker Verification
The pre-processing steps of linear discriminant analysis (LDA), unit length normalization and within class covariance normalization are all modeled as layers of a neural model and the speaker verification cost functions can be back-propagated through these layers during training.
