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A COMPARISON OF PREPROCESSORS FOR THE CAMBRIDGE RECURRENT ERROR PROPAGATION NETWORK SPEECH RECOGNITION SYSTEM
Tony Robinson, John Holdsworth, Roy Patterson and Frank Fallside
This paper makes a comparison of several preprocessors for the task of speaker independent phoneme recognition from the TIMIT database using a recurrent error propagation network recogniser (Robinson Fallside 90, TR42)
The paper evaluates FFT, filterbank, auditory model and LPC based techniques in the spectral and cepstral domains and adds some simple features such as estimates of the degree of voicing, formant positions and amplitudes. The paper concludes that the features do not make a significant contribution and that the spectral domain representations, independent of their derivation, are better suited to this task. However, we find that the recogniser was relatively insensitive to preprocessor and changes in the architecture and training of the recogniser are more significant.
The current recognition rate on the TIMIT database of 61 symbols is 69.5% correct (64.0% including insertion errors) and on a reduced 39 symbol set the recognition rate is 76.1% correct (70.4%). This compares favourably with the results of other methods, such as Hidden Markov Models, on the same task.
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