Unverified60% confidenceFactExact time
x-vector was proposed by Daniel Snyder et al. in 2018, using deep neural networks to extract fixed-dimensional speaker representation vectors from variable-length speech segments.
2
Sources
60%
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
Unverified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71% similarUnverifiedNeural network-based x-vector and d-vector embeddings have gradually replaced the traditional i-vector method, performing more stably in scenarios with frequent speaker changes.71% similarVerifiedModern speaker diarization systems typically combine speaker embeddings (such as d-vectors and x-vectors) with clustering algorithms.71% similarUnverified说话人嵌入向量由独立的说话人编码器网络（如d-vector或x-vector架构）生成，输出通常为128至512维的浮点数向量70% similarUnverifiedAzure Speech is built on Microsoft's deep neural network pipeline as Custom Neural Voice (CNV), with clear output and low latency.67% similar
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