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 301–328 of 328 papers

TitleStatusHype
Automating Feedback Analysis in Surgical Training: Detection, Categorization, and AssessmentCode0
Supervised Hierarchical Clustering using Graph Neural Networks for Speaker DiarizationCode0
Supervised online diarization with sample mean loss for multi-domain dataCode0
End-to-End Supervised Hierarchical Graph Clustering for Speaker DiarizationCode0
DiaCorrect: End-to-end error correction for speaker diarizationCode0
Robust speaker recognition using unsupervised adversarial invarianceCode0
Speaker Diarization using Two-pass Leave-One-Out Gaussian PLDA Clustering of DNN EmbeddingsCode0
Self-Supervised Metric Learning With Graph Clustering For Speaker DiarizationCode0
Self-supervised Representation Learning With Path Integral Clustering For Speaker DiarizationCode0
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial DomainCode0
Self-Tuning Spectral Clustering for Speaker DiarizationCode0
Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakersCode0
On the calibration of powerset speaker diarization modelsCode0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech RepresentationCode0
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count EstimationCode0
Speaker Embedding-aware Neural Diarization for Flexible Number of Speakers with Textual InformationCode0
The EURECOM Submission to the First DIHARD ChallengeCode0
Ultrasound tongue imaging for diarization and alignment of child speech therapy sessionsCode0
Multi-Stage Speaker Diarization for Noisy ClassroomsCode0
A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational AICode0
The Second DIHARD Diarization Challenge: Dataset, task, and baselinesCode0
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker IdentificationCode0
LSTM based Similarity Measurement with Spectral Clustering for Speaker DiarizationCode0
Long-term Conversation Analysis: Exploring Utility and PrivacyCode0
Probabilistic embeddings for speaker diarizationCode0
Visualizations of Complex Sequences of Family-Infant Vocalizations Using Bag-of-Audio-Words Approach Based on Wav2vec 2.0 FeaturesCode0
Fully Supervised Speaker DiarizationCode0
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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