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 101–150 of 328 papers

TitleStatusHype
Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization—0
Auxiliary Loss of Transformer with Residual Connection for End-to-End Speaker Diarization—0
基於i-vector與PLDA並使用GMM-HMM強制對位之自動語者分段標記系統 (Speaker Diarization based on I-vector PLDA Scoring and using GMM-HMM Forced Alignment) [In Chinese]—0
EEND-DEMUX: End-to-End Neural Speaker Diarization via Demultiplexed Speaker Embeddings—0
Chronological Self-Training for Real-Time Speaker Diarization—0
Generation of Speaker Representations Using Heterogeneous Training Batch Assembly—0
GIST-AiTeR Speaker Diarization System for VoxCeleb Speaker Recognition Challenge (VoxSRC) 2023—0
Guided Speaker Embedding—0
A framework for the automatic inference of stochastic turn-taking styles—0
Home monitoring for frailty detection through sound and speaker diarization analysis—0
ECAPA-TDNN Embeddings for Speaker Diarization—0
Implicit Self-supervised Language Representation for Spoken Language Diarization—0
Implicit spoken language diarization—0
Improving Diarization Robustness using Diversification, Randomization and the DOVER Algorithm—0
Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling—0
Improving Speaker Assignment in Speaker-Attributed ASR for Real Meeting Applications—0
Improving Speaker Diarization using Semantic Information: Joint Pairwise Constraints Propagation—0
Domain-Dependent Speaker Diarization for the Third DIHARD Challenge—0
Improving Transformer-based End-to-End Speaker Diarization by Assigning Auxiliary Losses to Attention Heads—0
Incorporating Spatial Cues in Modular Speaker Diarization for Multi-channel Multi-party Meetings—0
Indigenous language technologies in Canada: Assessment, challenges, and successes—0
Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization—0
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis—0
Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization—0
Investigating Confidence Estimation Measures for Speaker Diarization—0
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