Unverified50% confidenceBenchmarkExact time
The latest version of pyannote.audio adopts an end-to-end neural network architecture (EEND), and the diarization error rate (DER) on standard evaluation sets has dropped below 10%.
1
Sources
50%
Confidence
Long-term
Relevance
8/5/2026
First Seen
Sources
yapyap：本地优先的开源会议录制与转录工具
Related Claims
UnverifiedThe introduction of deep neural networks in 2012 caused speech recognition error rates to drop by more than 30%.73% similarUnverifiedThe latest version of pyannote.audio adopts an end-to-end neural network architecture (EEND), which can directly output speaker labels for each time frame from mixed speech and can handle overlapping speech scenarios.72% similarUnverified说话人分离的主流方案包括基于x-vector嵌入的聚类方法和端到端神经网络方法（如pyannote.audio），标准条件下准确率可达90%以上70% similar
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