x-vector：Deep Neural Network Embeddings for Text-Independent Speaker Verification
會議：2017 interspeech 機構：The Johns Hopkins University 作者：David Snyder abstract 用dnn提取的embedding取代i-vector表示說話人特徵，對輸入的speech做時間池化， 用不同長度的語音訓練網絡，以區別說話人，句子對應定長的embeddding,成對的embedding用於PLDA打分。在NIST SRE201
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相關文章
- 1. Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification
- 2. 2019--Target Speaker Extraction for Multi-Talker Speaker Verification
- 3. Signature Verification using a Siamese Time Delay Neural Network
- 4. icassp2020--TEXT-INDEPENDENT SPEAKER VERIFICATION WITH ADVERSARIAL LEARNING ON SHORT UTTERANCES
- 5. Deep Speaker: an End-to-End Neural Speaker Embedding System
- 6. X-VECTOR:ROBUST DNN EMBEDDING FOR SPEAKER RECOGNITION
- 7. Within-sample variability-invariant loss for robust speaker recognition under noisy environments
- 8. (zhuan) Recurrent Neural Network
- 9. Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
- 10. 翻譯-Support Vector Machines using GMM Supervectors for Speaker Verification