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 251–275 of 328 papers

TitleStatusHype
Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting—0
Spatial-Temporal Activity-Informed Diarization and Separation—0
Speaker-conversation factorial designs for diarization error analysis—0
Speaker Diarization and Identification from Single-Channel Classroom Audio Recording Using Virtual Microphones—0
Speaker conditioned acoustic modeling for multi-speaker conversational ASR—0
Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning—0
Speaker Diarization of Scripted Audiovisual Content—0
Speaker Diarization using Deep Recurrent Convolutional Neural Networks for Speaker Embeddings—0
Speaker diarization using latent space clustering in generative adversarial network—0
Utterance Clustering Using Stereo Audio Channels—0
Speaker Diarization With Lexical Information—0
Speaker Diarization with Lexical Information—0
Speaker diarization with session-level speaker embedding refinement using graph neural networks—0
Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios—0
Speaker Embeddings With Weakly Supervised Voice Activity Detection For Efficient Speaker Diarization—0
Speaker Mask Transformer for Multi-talker Overlapped Speech Recognition—0
Speaker Recognition Based on Deep Learning: An Overview—0
Speakers Unembedded: Embedding-free Approach to Long-form Neural Diarization—0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
Speech Trax: A Bottom to the Top Approach for Speaker Tracking and Indexing in an Archiving Context—0
Summary of the DISPLACE Challenge 2023 -- DIarization of SPeaker and LAnguage in Conversational Environments—0
Systematic Evaluation of Online Speaker Diarization Systems Regarding their Latency—0
System Description for the Displace Speaker Diarization Challenge 2023—0
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system—0
TalTech-IRIT-LIS Speaker and Language Diarization Systems for DISPLACE 2024—0
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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