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• W3146087165 abstract "With the successful application of deep speaker embedding networks, the performance of speaker verification systems has significantly improved under clean and close-talking settings; however, unsatisfactory performance persists under noisy and far-field environments. This study aims at improving the performance of far-field speaker verification systems with distributed microphone arrays in the smart home scenario. The proposed learning framework consists of two modules: a deep speaker embedding module and an aggregation module. The former extracts a speaker embedding for each recording. The latter, based on either averaged pooling or attentive pooling, aggregates speaker embeddings and learns a unified representation for all recordings captured by distributed microphone arrays. The two modules are trained in an end-to-end manner. To evaluate this framework, we conduct experiments on the real text-dependent far-field datasets Hi Mia. Results show that our framework outperforms the naive averaged aggregation methods by 20% in terms of equal error rate (EER) with six distributed microphone arrays. Also, we find that the attention-based aggregation advocates high-quality recordings and repels low-quality ones." @default.
• W3146087165 created "2021-04-13" @default.
• W3146087165 creator A5021384155 @default.
• W3146087165 creator A5090055875 @default.
• W3146087165 date "2021-01-19" @default.
• W3146087165 modified "2023-10-18" @default.
• W3146087165 title "Embedding Aggregation for Far-Field Speaker Verification with Distributed Microphone Arrays" @default.
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• W3146087165 doi "https://doi.org/10.1109/slt48900.2021.9383501" @default.
• W3146087165 hasPublicationYear "2021" @default.
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