Method of training neural networks used for speech recognition

Details Method of training neural networks used for speech recognition Method of training neural networks used for speech recognition

1994-06-03

1996-04-16

MacDonald, Allen R.

395 23, G10L 900

Patent

active

055091032

ABSTRACT:
A speech-recognition system for recognizing isolated words includes pre-processing circuitry for performing analog-to-digital conversion and cepstral analysis, and a plurality of neural networks which compute discriminant functions based on polynomial expansions. The system may be implemented using either hardware or software or a combination thereof. The speech wave-form of a spoken word is analyzed and converted into a sequence of data frames. The sequence of frames is partitioned into data blocks, and the data blocks are then broadcast to a plurality of neural networks. Using the data blocks, the neural networks compute polynomial expansions. The output of the neural networks is used to determine the identity of the spoken word. The neural networks utilize a matrix-inversion or alternatively a least-squares estimation training algorithm which does not require repetitive training and which yields a global minimum to each given set of training examples.

REFERENCES:
patent: 5365592 (1994-11-01), Horner et al.
patent: 5404422 (1995-04-01), Sakamoto et al.
patent: 5408586 (1995-04-01), Skeirik
patent: 5408588 (1995-04-01), Ulug
R. G. Gallager, Information Theory and Reliable Communication, Wiley, New York, 1968, pp. 286-290.

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