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 201–250 of 328 papers

TitleStatusHype
The USTC-NERCSLIP Systems for The ICMC-ASR Challenge—0
The USTC-Ximalaya system for the ICASSP 2022 multi-channel multi-party meeting transcription (M2MeT) challenge—0
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
The xmuspeech system for multi-channel multi-party meeting transcription challenge—0
Third DIHARD Challenge Evaluation Plan—0
"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)—0
Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network—0
Tight integration of neural- and clustering-based diarization through deep unfolding of infinite Gaussian mixture model—0
Toeplitz Inverse Covariance based Robust Speaker Clustering for Naturalistic Audio Streams—0
TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch—0
Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations—0
Late Audio-Visual Fusion for In-The-Wild Speaker Diarization—0
Towards Measuring and Scoring Speaker Diarization Fairness—0
Towards Robust Family-Infant Audio Analysis Based on Unsupervised Pretraining of Wav2vec 2.0 on Large-Scale Unlabeled Family Audio—0
Towards Unsupervised Speaker Diarization System for Multilingual Telephone Calls Using Pre-trained Whisper Model and Mixture of Sparse Autoencoders—0
Towards Word-Level End-to-End Neural Speaker Diarization with Auxiliary Network—0
Training Speaker Embedding Extractors Using Multi-Speaker Audio with Unknown Speaker Boundaries—0
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR—0
Triplet Network with Attention for Speaker Diarization—0
TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge—0
Uncertainty Quantification in Machine Learning for Joint Speaker Diarization and Identification—0
Unified Audio Event Detection—0
Universal Speaker Embedding Free Target Speaker Extraction and Personal Voice Activity Detection—0
UniX-Encoder: A Universal X-Channel Speech Encoder for Ad-Hoc Microphone Array Speech Processing—0
Unsupervised Adaptation of SPLDA—0
Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning—0
Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free—0
Using Active Speaker Faces for Diarization in TV shows—0
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones—0
UWB-NTIS Speaker Diarization System for the DIHARD II 2019 Challenge—0
VOXLINGUA107: A DATASET FOR SPOKEN LANGUAGE RECOGNITION—0
VoxRAG: A Step Toward Transcription-Free RAG Systems in Spoken Question Answering—0
Weakly Supervised Training of Speaker Identification Models—0
An approach to optimize inference of the DIART speaker diarization pipeline—0
X-Vectors with Multi-Scale Aggregation for Speaker Diarization—0
A Benchmark for Multi-speaker Anonymization—0
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio—0
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings—0
Advances in Online Audio-Visual Meeting Transcription—0
A framework for the automatic inference of stochastic turn-taking styles—0
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond—0
AG-LSEC: Audio Grounded Lexical Speaker Error Correction—0
Aligning Speakers: Evaluating and Visualizing Text-based Diarization Using Efficient Multiple Sequence Alignment (Extended Version)—0
All-neural online source separation, counting, and diarization for meeting analysis—0
An Alternative to Low-level-Sychrony-Based Methods for Speech Detection—0
An automated medical scribe for documenting clinical encounters—0
An Effortless Way To Create Large-Scale Datasets For Famous Speakers—0
An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings—0
An Infinite Hidden Markov Model With Similarity-Biased Transitions—0
基於i-vector與PLDA並使用GMM-HMM強制對位之自動語者分段標記系統 (Speaker Diarization based on I-vector PLDA Scoring and using GMM-HMM Forced Alignment) [In Chinese]—0
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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