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 1–50 of 328 papers

TitleStatusHype
PaddleSpeech: An Easy-to-Use All-in-One Speech ToolkitCode6
pyannote.audio: neural building blocks for speaker diarizationCode3
Leveraging Self-Supervised Learning for Speaker DiarizationCode3
DiarizationLM: Speaker Diarization Post-Processing with Large Language ModelsCode3
Efficient and Generalizable Speaker Diarization via Structured Pruning of Self-Supervised ModelsCode3
Highly Efficient Real-Time Streaming and Fully On-Device Speaker Diarization with Multi-Stage ClusteringCode2
PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker RecordingsCode2
Overlap-aware low-latency online speaker diarization based on end-to-end local segmentationCode2
DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech RecognitionCode2
Streaming Speaker-Attributed ASR with Token-Level Speaker EmbeddingsCode1
Low-Latency Speech Separation Guided Diarization for Telephone ConversationsCode1
Speaker Diarization: Using Recurrent Neural NetworksCode1
Speaker Diarization with Region Proposal NetworkCode1
Speech Recognition and Multi-Speaker Diarization of Long ConversationsCode1
Speaker Diarization with LSTMCode1
The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and BaselinesCode1
Exploring Speech Foundation Models for Speaker Diarization in Child-Adult Dyadic InteractionsCode1
End-to-End Neural Speaker Diarization with Self-attentionCode1
Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractorsCode1
Reformulating DOVER-Lap Label Mapping as a Graph Partitioning ProblemCode1
End-to-end speaker segmentation for overlap-aware resegmentationCode1
Enhancing Speaker Diarization with Large Language Models: A Contextual Beam Search ApproachCode1
LLM-based speaker diarization correction: A generalizable approachCode1
Improving the Naturalness of Simulated Conversations for End-to-End Neural DiarizationCode1
A Light Weight Model for Active Speaker DetectionCode1
Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level TasksCode1
Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence ArchitectureCode1
Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation AlgorithmCode1
Speech Emotion Diarization: Which Emotion Appears When?Code1
Multi-Stage Face-Voice Association Learning with Keynote Speaker DiarizationCode1
DiariST: Streaming Speech Translation with Speaker DiarizationCode1
DiaPer: End-to-End Neural Diarization with Perceiver-Based AttractorsCode1
Phoneme Boundary Detection using Learnable Segmental FeaturesCode1
Data Efficient Child-Adult Speaker Diarization with Simulated ConversationsCode1
Online speaker diarization of meetings guided by speech separationCode1
DiaCorrect: Error Correction Back-end For Speaker DiarizationCode1
Advances in integration of end-to-end neural and clustering-based diarization for real conversational speechCode1
From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural DiarizationCode1
Encoder-Decoder Based Attractors for End-to-End Neural DiarizationCode1
Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum EigengapCode1
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label ClassificationCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
AVA-AVD: Audio-Visual Speaker Diarization in the WildCode1
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based AttractorsCode1
BER: Balanced Error Rate For Speaker DiarizationCode1
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control CommunicationsCode1
Learning Disentangled Phone and Speaker Representations in a Semi-Supervised VQ-VAE ParadigmCode1
Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimationCode1
Speaker Diarization as a Fully Online Learning Problem in MiniVoxCode1
The Third DIHARD Diarization ChallengeCode1
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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