|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
SEVERAL IMPROVEMENTS TO A RECURRENT ERROR PROPAGATION NETWORK PHONE RECOGNITION SYSTEM
Recurrent Error Propagation Networks have been shown to give good performance on the speaker independent phone recognition task in comparison with other methods (Robinson and Fallside, 1991). This short report describes several recent improvements made to the existing recogniser for the TIMIT database.
The improvements are: an addition to the preprocessor to represent voicing information; use of histogram normalisation on the input channels of the network; normalisation of the output channels to enforce unity sum; a change in the cost function to give equal weighting to each target symbol; a change in the representation of the outputs to reduce quantisation errors; retraining on the complete TIMIT training set; and the better estimation of HMM phone models.
Most of these changes decrease the number of arbitrary parameters used and allow for the integration of the system with standard HMM techniques. The result of these changes is a decrease in the number of errors by about 16% (from 36.5% to 30.7% when all 61 TIMIT phones are used and from 30.2% to 25.0% on a reduced 39 phone set).
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