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 201250 of 328 papers

TitleStatusHype
Self-Supervised Metric Learning With Graph Clustering For Speaker DiarizationCode0
Overlap-aware low-latency online speaker diarization based on end-to-end local segmentationCode2
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker IdentificationCode0
The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge0
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation0
Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker0
A Real-time Speaker Diarization System Based on Spatial Spectrum0
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio0
Separation Guided Speaker Diarization in Realistic Mismatched Conditions0
Development of a Conversation State Prediction System0
Encoder-Decoder Based Attractors for End-to-End Neural DiarizationCode1
Speaker-conversation factorial designs for diarization error analysis0
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection0
DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding0
Advances in integration of end-to-end neural and clustering-based diarization for real conversational speechCode1
X-Vectors with Multi-Scale Aggregation for Speaker Diarization0
Self-supervised Representation Learning With Path Integral Clustering For Speaker DiarizationCode0
End-to-end speaker segmentation for overlap-aware resegmentationCode1
Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network0
Speaker Diarization using Two-pass Leave-One-Out Gaussian PLDA Clustering of DNN EmbeddingsCode0
LEAP Submission for the Third DIHARD Diarization Challenge0
Speaker conditioned acoustic modeling for multi-speaker conversational ASR0
Reformulating DOVER-Lap Label Mapping as a Graph Partitioning ProblemCode1
ECAPA-TDNN Embeddings for Speaker Diarization0
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial DomainCode0
Domain-Dependent Speaker Diarization for the Third DIHARD Challenge0
A Review of Speaker Diarization: Recent Advances with Deep Learning0
End-to-End Speaker Diarization as Post-Processing0
Speaker Recognition Based on Deep Learning: An Overview0
The Third DIHARD Diarization ChallengeCode1
A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational AICode0
VoxLingua107: a Dataset for Spoken Language RecognitionCode1
VOXLINGUA107: A DATASET FOR SPOKEN LANGUAGE RECOGNITION0
BW-EDA-EEND: Streaming End-to-End Neural Speaker Diarization for a Variable Number of Speakers0
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis0
Third DIHARD Challenge Evaluation Plan0
EML System Description for VoxCeleb Speaker Diarization Challenge 20200
Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakersCode0
Learning Disentangled Phone and Speaker Representations in a Semi-Supervised VQ-VAE ParadigmCode1
Novel Architectures for Unsupervised Information Bottleneck based Speaker Diarization of Meetings0
Utterance Clustering Using Stereo Audio Channels0
asya: Mindful verbal communication using deep learning0
"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)0
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones0
Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations0
Speaker Diarization: Using Recurrent Neural NetworksCode1
Speaker Diarization as a Fully Online Learning Problem in MiniVoxCode1
Online End-to-End Neural Diarization with Speaker-Tracing Buffer0
Neural Speaker Diarization with Speaker-Wise Chain RuleCode0
Speaker diarization with session-level speaker embedding refinement using graph neural networks0
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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