Speaker Diarization

Speaker Diarization is the task of segmenting and co-indexing audio recordings by speaker. The way the task is commonly defined, the goal is not to identify known speakers, but to co-index segments that are attributed to the same speaker; in other words, diarization implies finding speaker boundaries and grouping segments that belong to the same speaker, and, as a by-product, determining the number of distinct speakers. In combination with speech recognition, diarization enables speaker-attributed speech-to-text transcription.

Source: Improving Diarization Robustness using Diversification, Randomization and the DOVER Algorithm

Papers

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Showing 251–275 of 328 papers

Title	Date	Tasks	Status
Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakers	Oct 22, 2020	speaker-diarizationSpeaker Diarization	CodeCode Available
Novel Architectures for Unsupervised Information Bottleneck based Speaker Diarization of Meetings	Oct 13, 2020	Clusteringspeaker-diarization	—Unverified
Utterance Clustering Using Stereo Audio Channels	Sep 10, 2020	Audio Signal ProcessingClustering	—Unverified
asya: Mindful verbal communication using deep learning	Aug 20, 2020	Deep Learningspeaker-diarization	—Unverified
"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)	Aug 4, 2020	Action DetectionActivity Detection	—Unverified
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones	Jul 31, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations	Jul 1, 2020	Action DetectionActivity Detection	—Unverified
Online End-to-End Neural Diarization with Speaker-Tracing Buffer	Jun 4, 2020	speaker-diarizationSpeaker Diarization	—Unverified
Neural Speaker Diarization with Speaker-Wise Chain Rule	Jun 2, 2020	speaker-diarizationSpeaker Diarization	—Unverified
Speaker diarization with session-level speaker embedding refinement using graph neural networks	May 22, 2020	Clusteringspeaker-diarization	—Unverified
A Thousand Words are Worth More Than One Recording: NLP Based Speaker Change Point Detection	May 18, 2020	Change Point Detectionspeaker-diarization	—Unverified
Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario	May 14, 2020	Action DetectionActivity Detection	—Unverified
Semi-supervised Acoustic Modelling for Five-lingual Code-switched ASR using Automatically-segmented Soap Opera Speech	May 1, 2020	Acoustic ModellingAction Detection	—Unverified
Preparation of Bangla Speech Corpus from Publicly Available Audio \& Text	May 1, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings	Apr 20, 2020	speaker-diarizationSpeaker Diarization	—Unverified
Speaker Diarization with Lexical Information	Apr 13, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech	Apr 8, 2020	Acoustic ModellingAction Detection	—Unverified
Probabilistic embeddings for speaker diarization	Apr 6, 2020	Clusteringspeaker-diarization	CodeCode Available
ASR Error Correction and Domain Adaptation Using Machine Translation	Mar 13, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system	Mar 9, 2020	Allspeaker-diarization	—Unverified
Self-supervised learning for audio-visual speaker diarization	Feb 13, 2020	Self-Supervised Learningspeaker-diarization	—Unverified
Compositional Embeddings for Multi-Label One-Shot Learning	Feb 11, 2020	Object DetectionObject Recognition	—Unverified
Advances in Online Audio-Visual Meeting Transcription	Dec 10, 2019	Sound Source Localizationspeaker-diarization	—Unverified
The Speed Submission to DIHARD II: Contributions & Lessons Learned	Nov 6, 2019	Action DetectionActivity Detection	—Unverified
Supervised online diarization with sample mean loss for multi-domain data	Nov 4, 2019	Clusteringspeaker-diarization	CodeCode Available

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All datasets CALLHOME NIST-SRE 2000 AMI Lapel AMI MixHeadset CH109 DIHARD ETAPE AMI CALLHOME-109 AliMeeting DIHARD II Hub5'00 CallHome

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	COS+NJW-SC (Oracle SAD)	DER(%)	24.05	—	Unverified
2	EEND	DER(%)	23.07	—	Unverified
3	COS+AHC (Oracle SAD)	DER(%)	21.13	—	Unverified
4	SA-EEND (2-spk, no-adapt)	DER(%)	12.66	—	Unverified
5	EEND-OLA	DER(%)	12.57	—	Unverified
6	SA-EEND (2-spk, adapted)	DER(%)	10.76	—	Unverified
7	TOLD	DER(%)	10.14	—	Unverified
8	COS+B-SC (Oracle SAD)	DER(ig olp)	8.78	—	Unverified
9	PLDA+AHC (Oracle SAD)	DER(ig olp)	8.39	—	Unverified
10	COS+NME-SC (Oracle SAD)	DER(ig olp)	7.29	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	x-vector (PLDA + AHC)	DER(%)	8.39	—	Unverified
2	TitaNet-L (NME-SC)	DER(%)	6.73	—	Unverified
3	TitaNet-M (NME-SC)	DER(%)	6.47	—	Unverified
4	TitaNet-S (NME-SC)	DER(%)	6.37	—	Unverified
5	x-vector (MCGAN)	DER(%)	5.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ECAPA (SC)	DER(%)	2.36	—	Unverified
2	TitaNet-L (NME-SC)	DER(%)	2.03	—	Unverified
3	TitaNet-S (NME-SC)	DER(%)	2	—	Unverified
4	TitaNet-M (NME-SC)	DER(%)	1.99	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TitaNet-S (NME-SC)	DER(%)	2.22	—	Unverified
2	TitaNet-M (NME-SC)	DER(%)	1.79	—	Unverified
3	ECAPA (SC)	DER(%)	1.78	—	Unverified
4	TitaNet-L (NME-SC)	DER(%)	1.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	x-vector (PLDA + AHC)	DER(%)	9.72	—	Unverified
2	TitaNet-L (NME-SC)	DER(%)	1.19	—	Unverified
3	TitaNet-M (NME-SC)	DER(%)	1.13	—	Unverified
4	TitaNet-S (NME-SC)	DER(%)	1.11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Baseline (the best result in the literature as of Oct.2019)	DER(%)	11.2	—	Unverified
2	pyannote (MFCC)	DER(%)	10.5	—	Unverified
3	pyannote (waveform)	DER(%)	9.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Baseline	DER(%)	7.7	—	Unverified
2	pyannote (MFCC)	DER(%)	5.6	—	Unverified
3	pyannote (waveform)	DER(%)	4.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	pyannote (MFCC)	DER(%)	6.3	—	Unverified
2	pyannote (waveform)	DER(%)	6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	d-vector + spectral	DER(%)	12.54	—	Unverified
2	titanet-s	DER(%)	1.11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SOND	DER(%)	4.46	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UIS-RNN-SML	DER(%)	27.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UIS-RNN	V	10.6	—	Unverified