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- W4328100135 abstract "Abstract To improve the speech emotion recognition accuracy with availability of multi-level features, it is natural to fuse the emotion information and reduce the distribution mismatch of multi-level features simultaneously. To address the above issues, based on two constraints, we propose a novel transfer multiple kernel learning for fusing multi-level features (MFF-TMKL) . The first constraint aims at fusing the emotion information from multi-level features. Correspondingly, a weighted collaborative representation constraint is proposed to learn the similarity and distinctiveness of multi-level features by a space induced by the multiple kernel functions The second one aims at reducing the Maximum Mean Discrepancy of multi-level features also in this multiple kernel induced space. Experimental results on the Aibo emotional speech database show that:(1) MFF-TMKL achieves significant improvement than MKL, TMKL and MFF-TMKL with only one constraint. Specifically, using the utterance-level and unvoiced-level features, MFF-TMKL achieve the optimal recognition accuracy with 43.4% (2 ) using the utterance-level and unvoiced-level features, the recognition rate of MFF-TMKL is higher than the several state-of-the-art algorithms methods." @default.
- W4328100135 created "2023-03-22" @default.
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- W4328100135 date "2023-03-21" @default.
- W4328100135 modified "2023-09-27" @default.
- W4328100135 title "Speech Emotion Recognition Based on Simultaneously Fusing and Transferring of Multi-level Speech Features" @default.
- W4328100135 doi "https://doi.org/10.21203/rs.3.rs-2695951/v1" @default.
- W4328100135 hasPublicationYear "2023" @default.
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