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• W2399732698 abstract "This paper proposes to investigate speaker embeddings, a representation extracted from hidden layers of deep neural networks trained on a speaker identification task, on cross-show diarization. The new representation brings an improvement over i-vectors, and we show that while shallow hidden layers give best results on the single-show condition, deeper layers yield better performance on cross-show diarization. This confirms that deep representations model higher level features which help generalizing to different acoustic conditions. Experiments, conducted on the French corpus of REPERE, show that the deep speaker embeddings technique decreases DER by 0.82 points." @default.
• W2399732698 created "2016-06-24" @default.
• W2399732698 creator A5004769977 @default.
• W2399732698 creator A5071505335 @default.
• W2399732698 date "2016-03-01" @default.
• W2399732698 modified "2023-09-25" @default.
• W2399732698 title "Investigation of speaker embeddings for cross-show speaker diarization" @default.
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• W2399732698 doi "https://doi.org/10.1109/icassp.2016.7472746" @default.
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