SPEAKER ADAPTATION USING EIGENVOICES
The thesis considers a novel technique for adaptation of speaker models, called eigenvoice decomposition (ED), based around reducing the dimension of the search space of acoustic models. The technique is compared both practically and theoretically with several other adaptation techniques.
The use of Principal Component Analysis to choose the subspace is discussed and evaluated. One published method of choosing a model within this space, Maximal Likelihood Eigenvoice Decomposition, is presented and compared with a new method, Weighted Projection.
Robust estimation of the subspace was found to be difficult, but when it could be performed, ED was found to give a significant improvement in recognition accuracy with only one adaptation sentence, rather than the five or so required by other methods.
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