SOTAVerified

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 126–150 of 328 papers

TitleStatusHype
Joint speech and overlap detection: a benchmark over multiple audio setup and speech domains—0
An Infinite Hidden Markov Model With Similarity-Biased Transitions—0
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio—0
An approach to optimize inference of the DIART speaker diarization pipeline—0
Multimodal Clustering with Role Induced Constraints for Speaker Diarization—0
DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding—0
DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments—0
Autoapprentissage pour le regroupement en locuteurs : premi\`eres investigations (First investigations on self trained speaker diarization )—0
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation—0
Audiovisual speaker diarization of TV series—0
An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings—0
Meta-learning for robust child-adult classification from speech—0
Diarization-Aware Multi-Speaker Automatic Speech Recognition via Large Language Models—0
Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion—0
M3SD: Multi-modal, Multi-scenario and Multi-language Speaker Diarization Dataset—0
Long-Term Conversation Analysis: Privacy-Utility Trade-off under Noise and Reverberation—0
Audio-Visual Approach For Multimodal Concurrent Speaker Detection—0
An Effortless Way To Create Large-Scale Datasets For Famous Speakers—0
Advances in Online Audio-Visual Meeting Transcription—0
Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge—0
Matics Software Suite: New Tools for Evaluation and Data Exploration—0
Meeting Transcription Using Virtual Microphone Arrays—0
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR—0
An automated medical scribe for documenting clinical encounters—0
Listening to Multi-talker Conversations: Modular and End-to-end Perspectives—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COS+NJW-SC (Oracle SAD)DER(%)24.05—Unverified
2EENDDER(%)23.07—Unverified
3COS+AHC (Oracle SAD)DER(%)21.13—Unverified
4SA-EEND (2-spk, no-adapt)DER(%)12.66—Unverified
5EEND-OLADER(%)12.57—Unverified
6SA-EEND (2-spk, adapted)DER(%)10.76—Unverified
7TOLDDER(%)10.14—Unverified
8COS+B-SC (Oracle SAD)DER(ig olp)8.78—Unverified
9PLDA+AHC (Oracle SAD)DER(ig olp)8.39—Unverified
10COS+NME-SC (Oracle SAD)DER(ig olp)7.29—Unverified
#ModelMetricClaimedVerifiedStatus
1x-vector (PLDA + AHC)DER(%)8.39—Unverified
2TitaNet-L (NME-SC)DER(%)6.73—Unverified
3TitaNet-M (NME-SC)DER(%)6.47—Unverified
4TitaNet-S (NME-SC)DER(%)6.37—Unverified
5x-vector (MCGAN)DER(%)5.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ECAPA (SC)DER(%)2.36—Unverified
2TitaNet-L (NME-SC)DER(%)2.03—Unverified
3TitaNet-S (NME-SC)DER(%)2—Unverified
4TitaNet-M (NME-SC)DER(%)1.99—Unverified
#ModelMetricClaimedVerifiedStatus
1TitaNet-S (NME-SC)DER(%)2.22—Unverified
2TitaNet-M (NME-SC)DER(%)1.79—Unverified
3ECAPA (SC)DER(%)1.78—Unverified
4TitaNet-L (NME-SC)DER(%)1.73—Unverified
#ModelMetricClaimedVerifiedStatus
1x-vector (PLDA + AHC)DER(%)9.72—Unverified
2TitaNet-L (NME-SC)DER(%)1.19—Unverified
3TitaNet-M (NME-SC)DER(%)1.13—Unverified
4TitaNet-S (NME-SC)DER(%)1.11—Unverified
#ModelMetricClaimedVerifiedStatus
1Baseline (the best result in the literature as of Oct.2019)DER(%)11.2—Unverified
2pyannote (MFCC)DER(%)10.5—Unverified
3pyannote (waveform)DER(%)9.9—Unverified
#ModelMetricClaimedVerifiedStatus
1BaselineDER(%)7.7—Unverified
2pyannote (MFCC)DER(%)5.6—Unverified
3pyannote (waveform)DER(%)4.9—Unverified
#ModelMetricClaimedVerifiedStatus
1pyannote (MFCC)DER(%)6.3—Unverified
2pyannote (waveform)DER(%)6—Unverified
#ModelMetricClaimedVerifiedStatus
1d-vector + spectralDER(%)12.54—Unverified
2titanet-sDER(%)1.11—Unverified
#ModelMetricClaimedVerifiedStatus
1SONDDER(%)4.46—Unverified
#ModelMetricClaimedVerifiedStatus
1UIS-RNN-SMLDER(%)27.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UIS-RNNV10.6—Unverified