Title :
Global discrimination for neural predictive systems based on N-best algorithm
Author :
Mellouk, Abdelhamid ; Gallinari, Patrick
Author_Institution :
LRI, CNRS, Orsay, France
Abstract :
We describe a general formalism for training neural predictive systems. We then introduce discrimination at the frame level and show how it relates to maximum mutual information training. Finally, we propose an approach for performing discrimination in predictive systems at the sequence level, it makes use of N-best sequence selection. The performance for acoustic-phonetic decoding showed a 77.4% phone accuracy on the 1988 version of the TIMIT database
Keywords :
acoustic signal processing; decoding; learning (artificial intelligence); neural nets; prediction theory; speech processing; speech recognition; N-best algorithm; N-best sequence selection; TIMIT database; acoustic-phonetic decoding; frame level discrimination; global discrimination; maximum mutual information training; neural predictive systems; phone accuracy; sequence level discrimination; training; Context modeling; Decoding; Dynamic programming; Iterative algorithms; Mutual information; Neural networks; Predictive models; Production systems; Signal processing; Speech processing; Speech recognition;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-2431-5
DOI :
10.1109/ICASSP.1995.479629
