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- W2349343873 abstract "Effects of the non-context features, such as gender, speaker group identity, speaking rate and channel, for the classification and modeling of the speech data are studied based on data clustering and pre-classification knowledge methods. In order to incorporate non-context features with the context ones in the modeling process, generalized feature decision tree scheme is adopted and extended for the building of multiple high resolution acoustic models. Maximum likelihood model combination is then advanced to solve the subsequent model selection problem. Experimental results on two sets indicated that 8" @default.
- W2349343873 created "2016-06-24" @default.
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- W2349343873 date "2004-01-01" @default.
- W2349343873 modified "2023-09-23" @default.
- W2349343873 title "Integrating non-context features in speech data classification and modeling" @default.
- W2349343873 hasPublicationYear "2004" @default.
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