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- W1968492823 abstract "Traditional MFCC phonetic feature will lead a slower learning speed on account of it has high dimension and is large in data quantities. In order to solve this problem, we introduce a manifold learning, putting forward a new extraction method of MFCC-Manifold phonetic feature. We can reduce dimensions by making use of ISOMAP algorithm which bases on the classical MDS (Multidimensional scaling). Introducing geodesic distance to replace the original European distance data will make twenty-four dimensional data, which using the traditional MFCC feature extraction down to two dimensional data. Experiments prove that MFCC - Manifold feature extraction methods has achieved a satisfactory effect in data volume reduction" @default.
- W1968492823 created "2016-06-24" @default.
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- W1968492823 date "2013-01-01" @default.
- W1968492823 modified "2023-09-22" @default.
- W1968492823 title "The application research of speech feature extraction based on the manifold learning" @default.
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- W1968492823 doi "https://doi.org/10.2991/iccsee.2013.201" @default.
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