Ccu106 发表于 2025-3-25 05:19:45

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联邦 发表于 2025-3-25 09:24:09

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Distribution 发表于 2025-3-25 12:58:25

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过去分词 发表于 2025-3-25 17:03:53

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myelography 发表于 2025-3-25 21:36:54

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的事物 发表于 2025-3-26 00:24:06

Book 2014odels, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.

Genetics 发表于 2025-3-26 05:34:55

Robust Speaker Modeling for Speaker Verification in Noisy Environments, (SVMs)). For improving the performance of the proposed speaker verification systems, utterance partitioning methods are used. The discussion is closely followed by state-of-the-art variants of GMM supervector based approaches (i.e., i-vectors) and algorithms for combining robust classifiers.

Condyle 发表于 2025-3-26 11:59:56

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忍耐 发表于 2025-3-26 12:43:15

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内向者 发表于 2025-3-26 20:33:54

Speaker Verification in Noisy Environments Using Gaussian Mixture Models,an acoustic modeling framework (namely GMM-UBM) using speaker-dependent GMMs and a speaker-independent Universal Background Model (UBM), is studied for simulated noisy backgrounds. Significance of a feature mapping technique using multiple UBMs for compensating background noise is explored. The spea
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查看完整版本: Titlebook: Robust Speaker Recognition in Noisy Environments; K. Sreenivasa Rao,Sourjya Sarkar Book 2014 The Author(s) 2014 Feature Compensation using