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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 328 papers

TitleStatusHype
TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch—0
Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding—0
Automating Feedback Analysis in Surgical Training: Detection, Categorization, and AssessmentCode0
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation—0
Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation—0
DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions—0
Guided Speaker Embedding—0
Incorporating Spatial Cues in Modular Speaker Diarization for Multi-channel Multi-party Meetings—0
On the calibration of powerset speaker diarization modelsCode0
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR—0
Self-Tuning Spectral Clustering for Speaker DiarizationCode0
TCG CREST System Description for the Second DISPLACE Challenge—0
Leveraging Self-Supervised Learning for Speaker DiarizationCode3
Unified Audio Event Detection—0
Data Efficient Child-Adult Speaker Diarization with Simulated ConversationsCode1
Sortformer: Seamless Integration of Speaker Diarization and ASR by Bridging Timestamps and TokensCode0
A Toolkit for Joint Speaker Diarization and Identification with Application to Speaker-Attributed ASR—0
LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization—0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
Recursive Attentive Pooling for Extracting Speaker Embeddings from Multi-Speaker Recordings—0
Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization—0
An approach to optimize inference of the DIART speaker diarization pipeline—0
Long-Term Conversation Analysis: Privacy-Utility Trade-off under Noise and Reverberation—0
Multi-Stage Face-Voice Association Learning with Keynote Speaker DiarizationCode1
Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning—0
Show:102550
← PrevPage 2 of 14Next →
All datasetsCALLHOMENIST-SRE 2000AMI LapelAMI MixHeadsetCH109DIHARDETAPEAMICALLHOME-109AliMeetingDIHARD IIHub5'00 CallHome

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