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

Showing 251300 of 328 papers

TitleStatusHype
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based AttractorsCode1
A Thousand Words are Worth More Than One Recording: NLP Based Speaker Change Point Detection0
Speech Recognition and Multi-Speaker Diarization of Long ConversationsCode1
Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario0
Preparation of Bangla Speech Corpus from Publicly Available Audio \& Text0
Semi-supervised Acoustic Modelling for Five-lingual Code-switched ASR using Automatically-segmented Soap Opera Speech0
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings0
Speaker Diarization with Lexical Information0
Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech0
Probabilistic embeddings for speaker diarizationCode0
ASR Error Correction and Domain Adaptation Using Machine Translation0
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system0
Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum EigengapCode1
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label ClassificationCode1
Speaker Diarization with Region Proposal NetworkCode1
Self-supervised learning for audio-visual speaker diarization0
Compositional Embeddings for Multi-Label One-Shot Learning0
Phoneme Boundary Detection using Learnable Segmental FeaturesCode1
Advances in Online Audio-Visual Meeting Transcription0
The Speed Submission to DIHARD II: Contributions & Lessons Learned0
pyannote.audio: neural building blocks for speaker diarizationCode3
Supervised online diarization with sample mean loss for multi-domain dataCode0
Robust speaker recognition using unsupervised adversarial invarianceCode0
Meta-learning for robust child-adult classification from speech0
Speaker diarization using latent space clustering in generative adversarial network0
Improving Diarization Robustness using Diversification, Randomization and the DOVER Algorithm0
Compositional Embeddings: Joint Perception and Comparison of Class Label Sets0
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models0
End-to-End Neural Speaker Diarization with Self-attentionCode1
End-to-End Neural Speaker Diarization with Permutation-Free ObjectivesCode0
LSTM based Similarity Measurement with Spectral Clustering for Speaker DiarizationCode0
Toeplitz Inverse Covariance based Robust Speaker Clustering for Naturalistic Audio Streams0
Joint Speech Recognition and Speaker Diarization via Sequence Transduction0
Ultrasound tongue imaging for diarization and alignment of child speech therapy sessionsCode0
Large-Scale Speaker Diarization of Radio Broadcast Archives0
The Second DIHARD Diarization Challenge: Dataset, task, and baselinesCode0
UWB-NTIS Speaker Diarization System for the DIHARD II 2019 Challenge0
Meeting Transcription Using Virtual Microphone Arrays0
Latent Class Model with Application to Speaker Diarization0
All-neural online source separation, counting, and diarization for meeting analysis0
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Détection de locuteurs dans les séries TV0
Audiovisual speaker diarization of TV series0
Constrained speaker diarization of TV series based on visual patterns0
Speaker Diarization With Lexical Information0
Designing an Effective Metric Learning Pipeline for Speaker Diarization0
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count EstimationCode0
Semi-supervised acoustic model training for speech with code-switching0
Fully Supervised Speaker DiarizationCode0
The EURECOM Submission to the First DIHARD ChallengeCode0
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Benchmark Results

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