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 1–10 of 328 papers

TitleStatusHype
Efficient and Generalizable Speaker Diarization via Structured Pruning of Self-Supervised ModelsCode3
M3SD: Multi-modal, Multi-scenario and Multi-language Speaker Diarization Dataset—0
Exploring Speaker Diarization with Mixture of Experts—0
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models—0
SC-SOT: Conditioning the Decoder on Diarized Speaker Information for End-to-End Overlapped Speech Recognition—0
Diarization-Aware Multi-Speaker Automatic Speech Recognition via Large Language Models—0
Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling—0
Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation AlgorithmCode1
Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization—0
Pretraining Multi-Speaker Identification for Neural Speaker Diarization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UIS-RNN-SMLDER(%)27.3—Unverified