Vowel recognition with neural networks

O. Kost, A. Cohen

Research output: Contribution to conferencePaperpeer-review

Abstract

A comparison between two Neural Networks (NN) is presented. The Multi-Layer-Perceptron (MLP) which is a deterministic NN and the Boltzmann machine (BM) - a stochastic NN, both are suited for classification tasks. The networks performance is compared using both synthesized gaussian data and real speech. The results show that the BM converges in less learning cycles than the MLP. The MLP's excess error is, however, smaller than the BM's. Both networks perform worse than the gaussian maximum log likelihood classifier on steady state vowel utterances' classification.

Original languageEnglish
DOIs
StatePublished - 1 Jan 1989
Event16th Conference of Electrical and Electronics Engineers in Israel, EEIS 1989 - Tel-Aviv, Israel
Duration: 7 Mar 19899 Mar 1989

Conference

Conference16th Conference of Electrical and Electronics Engineers in Israel, EEIS 1989
Country/TerritoryIsrael
CityTel-Aviv
Period7/03/899/03/89

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Instrumentation

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