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 51–75 of 328 papers

TitleStatusHype
Improving the Naturalness of Simulated Conversations for End-to-End Neural DiarizationCode1
Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn DetectionCode1
Utterance-by-utterance overlap-aware neural diarization with Graph-PITCode1
VoxLingua107: a Dataset for Spoken Language RecognitionCode1
DiariST: Streaming Speech Translation with Speaker DiarizationCode1
Speech Emotion Diarization: Which Emotion Appears When?Code1
All-neural online source separation, counting, and diarization for meeting analysis—0
Constrained speaker diarization of TV series based on visual patterns—0
Computer-assisted Speaker Diarization: How to Evaluate Human Corrections—0
Assessing the Robustness of Spectral Clustering for Deep Speaker Diarization—0
A sticky HDP-HMM with application to speaker diarization—0
Cross-Channel Attention-Based Target Speaker Voice Activity Detection: Experimental Results for M2MeT Challenge—0
ECAPA-TDNN Embeddings for Speaker Diarization—0
Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding—0
Compositional Embeddings: Joint Perception and Comparison of Class Label Sets—0
ASR Error Correction and Domain Adaptation Using Machine Translation—0
Compositional Embeddings for Multi-Label One-Shot Learning—0
ASoBO: Attentive Beamformer Selection for Distant Speaker Diarization in Meetings—0
Aligning Speakers: Evaluating and Visualizing Text-based Diarization Using Efficient Multiple Sequence Alignment (Extended Version)—0
Domain-Dependent Speaker Diarization for the Third DIHARD Challenge—0
EEND-DEMUX: End-to-End Neural Speaker Diarization via Demultiplexed Speaker Embeddings—0
A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification—0
Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization—0
A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification.—0
Chronological Self-Training for Real-Time Speaker Diarization—0
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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