activity
20192024
most citedThe Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap

27 citations · 35 across the 7 of their papers we have counts for

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11 papers · 1 filter

eess.AS20222 cited

Adapting self-supervised models to multi-talker speech recognition using speaker embeddings

Zili Huang, Desh Raj, Paola García +1

Self-supervised learning (SSL) methods which learn representations of data without explicit supervision have gained popularity in speech-processing tasks, particularly for single-t…

eess.AS2021

Injecting Text and Cross-lingual Supervision in Few-shot Learning from Self-Supervised Models

Matthew Wiesner, Desh Raj, Sanjeev Khudanpur

Self-supervised model pre-training has recently garnered significant interest, but relatively few efforts have explored using additional resources in fine-tuning these models. We d…

eess.AS20212 cited

Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker

Maokui He, Desh Raj, Zili Huang +3

Target-speaker voice activity detection (TS-VAD) has recently shown promising results for speaker diarization on highly overlapped speech. However, the original model requires a fi…

eess.AS2021

Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem

Desh Raj, Sanjeev Khudanpur

We recently proposed DOVER-Lap, a method for combining overlap-aware speaker diarization system outputs. DOVER-Lap improved upon its predecessor DOVER by using a label mapping meth…

eess.AS202127 cited

The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap

Shota Horiguchi, Nelson Yalta, Paola Garcia +7

This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge. The system outputs the ensemble results of…

eess.AS2020

Multi-class Spectral Clustering with Overlaps for Speaker Diarization

Desh Raj, Zili Huang, Sanjeev Khudanpur

This paper describes a method for overlap-aware speaker diarization. Given an overlap detector and a speaker embedding extractor, our method performs spectral clustering of segment…