Speech recognition method
First Claim
1. A speech recognition method in which a basic set of models, which comprises models for various acoustic units, while the models are described by a plurality of model parameters, is adapted to a current speaker based on already observed speech data of this speaker, characterized in that the basic set of models is represented by a supervector in a high-dimensional vector space (model space), where the supervector is formed by concatenation of the plurality of model parameters of the models of the basic set of models, and this basic set of models in the model space is adapted to the speakers by means of a MAP method, while an asymmetrical distribution in the model space is chosen as an a priori distribution for the MAP method.
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Abstract
A speech recognition method is described in which a basic set of models is adapted to a current speaker on account of the speaker'"'"'s already noticed speech data. The basic set of models comprises models for different acoustic units. The models are described each by a plurality of model parameters. The basic set of models is then represented by a supervector in a high-dimensional vector space (model space), the supervector being formed by a concatenation of the plurality of the model parameters of the models of the basic set of models. The adaptation of this basic set of models to the speaker is effected in the model space by means of a MAP method in which an asymmetric distribution in the model space is selected as an a priori distribution for the MAP method.
17 Citations
13 Claims
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1. A speech recognition method in which a basic set of models, which comprises models for various acoustic units, while the models are described by a plurality of model parameters, is adapted to a current speaker based on already observed speech data of this speaker, characterized
in that the basic set of models is represented by a supervector in a high-dimensional vector space (model space), where the supervector is formed by concatenation of the plurality of model parameters of the models of the basic set of models, and this basic set of models in the model space is adapted to the speakers by means of a MAP method, while an asymmetrical distribution in the model space is chosen as an a priori distribution for the MAP method.
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