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 151–200 of 328 papers

TitleStatusHype
Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis—0
The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge—0
Implicit Self-supervised Language Representation for Spoken Language Diarization—0
Home monitoring for frailty detection through sound and speaker diarization analysis—0
GIST-AiTeR Speaker Diarization System for VoxCeleb Speaker Recognition Challenge (VoxSRC) 2023—0
Speaker Diarization of Scripted Audiovisual Content—0
Joint speech and overlap detection: a benchmark over multiple audio setup and speech domains—0
Semi-supervised multi-channel speaker diarization with cross-channel attention—0
Long-term Conversation Analysis: Exploring Utility and PrivacyCode0
Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization—0
Implicit spoken language diarization—0
Lexical Speaker Error Correction: Leveraging Language Models for Speaker Diarization Error Correction—0
Multi-microphone Automatic Speech Segmentation in Meetings Based on Circular Harmonics Features—0
An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings—0
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization—0
Exploring Speaker-Related Information in Spoken Language Understanding for Better Speaker Diarization—0
Towards Robust Family-Infant Audio Analysis Based on Unsupervised Pretraining of Wav2vec 2.0 on Large-Scale Unlabeled Family Audio—0
Neural Diarization with Non-autoregressive Intermediate AttractorsCode0
TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker DiarizationCode0
Improving Transformer-based End-to-End Speaker Diarization by Assigning Auxiliary Losses to Attention Heads—0
DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments—0
Supervised Hierarchical Clustering using Graph Neural Networks for Speaker DiarizationCode0
A Reinforcement Learning Framework for Online Speaker Diarization—0
Towards Measuring and Scoring Speaker Diarization Fairness—0
The Newsbridge -Telecom SudParis VoxCeleb Speaker Recognition Challenge 2022 System Description—0
Late Audio-Visual Fusion for In-The-Wild Speaker Diarization—0
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings—0
DiaCorrect: End-to-end error correction for speaker diarizationCode0
Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction—0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
Privacy-preserving Automatic Speaker Diarization—0
TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge—0
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization—0
Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting—0
Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization—0
Chronological Self-Training for Real-Time Speaker Diarization—0
Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free—0
Online Target Speaker Voice Activity Detection for Speaker Diarization—0
Speaker Diarization and Identification from Single-Channel Classroom Audio Recording Using Virtual Microphones—0
Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization—0
Simultaneous Speech Extraction for Multiple Target Speakers under the Meeting Scenarios—0
Audio-video fusion strategies for active speaker detection in meetings—0
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors—0
Bazinga! A Dataset for Multi-Party Dialogues Structuring—0
A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification.—0
Bi-LSTM Scoring Based Similarity Measurement with Agglomerative Hierarchical Clustering (AHC) for Speaker Diarization—0
Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure—0
Self-supervised Speaker Diarization—0
Multimodal Clustering with Role Induced Constraints for Speaker Diarization—0
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of SpeakersCode0
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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