Vowel recognition with neural networks

O. Kost; A. Cohen

doi:10.1109/EEIS.1989.720125

Vowel recognition with neural networks

O. Kost, A. Cohen

Department of Electrical & Computer Engineering

Research output: Contribution to conference › Paper › peer-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 language	English
DOIs	https://doi.org/10.1109/EEIS.1989.720125
State	Published - 1 Jan 1989
Event	16th Conference of Electrical and Electronics Engineers in Israel, EEIS 1989 - Tel-Aviv, Israel Duration: 7 Mar 1989 → 9 Mar 1989

Conference

Conference	16th Conference of Electrical and Electronics Engineers in Israel, EEIS 1989
Country/Territory	Israel
City	Tel-Aviv
Period	7/03/89 → 9/03/89

ASJC Scopus subject areas

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

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10.1109/EEIS.1989.720125

Cite this

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title = "Vowel recognition with neural networks",

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.",

author = "O. Kost and A. Cohen",

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AB - 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.

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Vowel recognition with neural networks

Abstract

Conference

ASJC Scopus subject areas

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