Method of determining an eigenspace for representing a...

Details Method of determining an eigenspace for representing a... Method of determining an eigenspace for representing a...

2005-07-05

2005-07-05

Abebe, Daniel (Department: 2655)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S255000

Reexamination Certificate

active

06915260

ABSTRACT:
Described here is a method of determining an eigenspace for representing a plurality of training speakers, in which first speaker-dependent sets of models are formed for the individual training speakers while training speech data of the individual training speakers are used and the models (SD) of a set of models are described each by a plurality of model parameters. For each speaker a combined model is then displayed in a high-dimensional model space by concatenation of the model parameters of the models of the individual training speakers to a respective coherent super vector. Subsequently, a transformation is carried out while the model space dimension is reduced to obtain eigenspace basis vectors (Ee), which transformation utilizes a reduction criterion based on the variability of the vectors to be transformed. Then the high-dimensional model space is first in a first step reduced to a speaker subspace by a change of basis, in which speaker subspace all the training speakers are represented. In a second step, inside this speaker subspace, the transformation is applied to the vectors representing the training speakers to obtain the eigenspace basis vectors (Ee).

REFERENCES:
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patent: 6343267 (2002-01-01), Kuhn et al.
patent: 6571208 (2003-05-01), Kuhn et al.

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