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- W3015544776 abstract "This paper proposes a general post-processing method for improving speaker-attribute estimation. Estimating speaker-specific attributes such as age and gender is an important task with a wide range of applications. While the recent proposed deep neural network-based end-to-end approach achieves high performance, the model tends to over-fit to specific speakers when the amount of training data is limited or imbalanced. To solve this over-fitting problem, we propose a general framework for correcting unreliable results. The proposed algorithm first clusters the target utterances into speaker clusters by speaker similarity based on i-vectors. Then, for each of the speaker cluster, the speaker-attribute class of the cluster is determined by voting on the utterances assigned to the cluster. By then replacing the result of each utterance with the clusters' speaker-attribute class, we can correct the result of unreliable utterances. We used two tasks to evaluate the proposed algorithm including age estimation using the NIST-SRE10 and age-gender classification using an in-house read speech corpus, yielding significant improvements in mean absolute and classification errors." @default.
- W3015544776 created "2020-04-17" @default.
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- W3015544776 date "2020-05-01" @default.
- W3015544776 modified "2023-10-14" @default.
- W3015544776 title "Improving Speaker-Attribute Estimation by Voting Based on Speaker Cluster Information" @default.
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- W3015544776 doi "https://doi.org/10.1109/icassp40776.2020.9053583" @default.
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