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- W2103443796 abstract "A technique for adapting speaker-independent speech recognition models to the voice of a new speaker is presented. The technique is capable of estimating adapted parameters for all the speech models when only a small subset of the recognition vocabulary is spoken by the new speaker. Whereas previous methods have often assumed a transformation between the speaker-independent models and the adapted models, this technique models the relationship between different speech units using linear regression. The regression models are built off-line using the training-set data. At recognition-time, the speech models are adapted using the regression models and the new speaker's data, a procedure which is computationally cheap. Experimental results show a halving of the recognition error-rate when only about 8% of the vocabulary is given as enrollment data, and when half the vocabulary is given, a reduction in the error-rate of 78%." @default.
- W2103443796 created "2016-06-24" @default.
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- W2103443796 date "2002-11-19" @default.
- W2103443796 modified "2023-09-23" @default.
- W2103443796 title "A speaker adaptation technique using linear regression" @default.
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- W2103443796 doi "https://doi.org/10.1109/icassp.1995.479790" @default.
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