Automatic Recognition of Italian I-Set by Recurrent Neural Networks

In order to prove the potential power of "learning by examples" paradigm for
problems of Automatic Speech Recognition, an experiment is described, regarding an
extremely difficult Italian phonetic recognition problem:
the automatic discrimination of the so called Italian i-set:
/bi/, /tSi/, /di/, /dZi/, /i/, /pi/, /ti/, /vi/
other two i-like stimuli /Li/, /si/.
Auditory Modeling is used as front-end digital signal processing. Semi-automatic
Multi-Level segmentation is applied to input speech stimuli. Recurrent Neural Networks
trained by Extended Back Propagation for Sequences constitute the global recognition
framework.. The achieved speaker independent mean recognition rate is around 65%
which, given the effective difficulty of the present task, can be considered quite acceptable
and promising.

Tipo Pubblicazione: 
Contributo in atti di convegno
Author or Creator: 
P. Cosi
P. Frasconi
M. Gori
N. Griggio
Edizioni LINT, Trieste, ITA
Proceedings Secondo Workshop su reti neuronali per il riconoscimento della voce, pp. 149–161, Firenze, Italy, December, 10-11, 1992
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ISTC Author: 
Ritratto di Piero Cosi
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