Papers › Deep Speaker: an End-to-End Neural Speaker Embedding System
Deep Speaker: an End-to-End Neural Speaker Embedding System
Chao Li, Xiaokong Ma, Bing Jiang, Xiangang Li, Xuewei Zhang, Xiao Liu, Ying Cao, Ajay Kannan, Zhenyao Zhu
We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering. We experiment with ResCNN and GRU architectures to extract the acoustic features, then mean pool to produce utterance-level speaker embeddings, and train using triplet loss based on cosine similarity. Experiments on three distinct datasets suggest that Deep Speaker outperforms a DNN-based i-vector baseline. For example, Deep Speaker reduces the verification equal error rate by 50% (relatively) and improves the identification accuracy by 60% (relatively) on a text-independent dataset. We also present results that suggest adapting from a model trained with Mandarin can improve accuracy for English speaker recognition.
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Code
Syntology Ran 7 of 9 code samples harvested from 4 repositories linked to this paper; 2 have no recorded run. Of those that ran: 2 ran · honoured contract; 4 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
9 samples harvested; 7 ran; 2 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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