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
Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimationCode1
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
The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and BaselinesCode1
Chronological Self-Training for Real-Time Speaker Diarization—0
Utterance-by-utterance overlap-aware neural diarization with Graph-PITCode1
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
A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification.—0
Bazinga! A Dataset for Multi-Party Dialogues Structuring—0
PaddleSpeech: An Easy-to-Use All-in-One Speech ToolkitCode6
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
Improving the Naturalness of Simulated Conversations for End-to-End Neural DiarizationCode1
Self-supervised Speaker Diarization—0
Low-Latency Speech Separation Guided Diarization for Telephone ConversationsCode1
From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural DiarizationCode1
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
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset—0
Generation of Speaker Representations Using Heterogeneous Training Batch Assembly—0
Streaming Speaker-Attributed ASR with Token-Level Speaker EmbeddingsCode1
Multi-scale Speaker Diarization with Dynamic Scale Weighting—0
Using Active Speaker Faces for Diarization in TV shows—0
Training Speaker Embedding Extractors Using Multi-Speaker Audio with Unknown Speaker Boundaries—0
Visualizations of Complex Sequences of Family-Infant Vocalizations Using Bag-of-Audio-Words Approach Based on Wav2vec 2.0 FeaturesCode0
Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting ScenariosCode0
Tight integration of neural- and clustering-based diarization through deep unfolding of infinite Gaussian mixture model—0
The xmuspeech system for multi-channel multi-party meeting transcription challenge—0
The USTC-Ximalaya system for the ICASSP 2022 multi-channel multi-party meeting transcription (M2MeT) challenge—0
Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge—0
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
Cross-Channel Attention-Based Target Speaker Voice Activity Detection: Experimental Results for M2MeT Challenge—0
The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
AVA-AVD: Audio-Visual Speaker Diarization in the WildCode1
Speaker Embedding-aware Neural Diarization for Flexible Number of Speakers with Textual InformationCode0
Low-Latency Online Speaker Diarization with Graph-Based Label Generation—0
Auxiliary Loss of Transformer with Residual Connection for End-to-End Speaker Diarization—0
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control CommunicationsCode1
Multi-Channel End-to-End Neural Diarization with Distributed Microphones—0
TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global contextCode1
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR—0
North America Bixby Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2021—0
Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn DetectionCode1
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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