METHOD OF DERIVING A COMPRESSED ACOUSTIC MODEL FOR SPEECH RECOGNITION
First Claim
1. A method of deriving a compressed acoustic model for speech recognition, the method comprising(i) transforming an acoustic model into eigen space to obtain eigenvectors of the acoustic model and their eigenvalues,(ii) determining predominant characteristics based on the eigenvalues of every dimension of each eigenvector;
- and(iii) selectively encoding the dimensions based on the predominant characteristics to obtain the compressed acoustic model.
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Abstract
A method of deriving a compressed acoustic model for speech recognition is disclosed herein. In a described embodiment, the method comprises transforming an acoustic model into an eigenspace at step 20, determining eigenvectors of the eigenspace and their eigenvalues, and selectively encoding dimensions of the eigenvectors based on values of the eigenspace at step 30 to obtain a compressed acoustic model at steps 40 and 50.
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9 Claims
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1. A method of deriving a compressed acoustic model for speech recognition, the method comprising
(i) transforming an acoustic model into eigen space to obtain eigenvectors of the acoustic model and their eigenvalues, (ii) determining predominant characteristics based on the eigenvalues of every dimension of each eigenvector; - and
(iii) selectively encoding the dimensions based on the predominant characteristics to obtain the compressed acoustic model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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