Multi-input Multi-output Beta Wavelet Network: Modeling of Acoustic Units for Speech Recognition

TL;DR

This paper introduces MIMOWN, a new wavelet network architecture designed to improve speech recognition by effectively modeling acoustic units through multi-input multi-output capabilities.

Contribution

The paper presents MIMOWN, a novel wavelet network architecture that generalizes previous models and enhances acoustic unit modeling for speech recognition.

Findings

MIMOWN effectively models acoustic units in speech recognition.

The architecture overcomes limitations of previous wavelet networks.

Improved training on diverse acoustic examples.

Abstract

In this paper, we propose a novel architecture of wavelet network called Multi-input Multi-output Wavelet Network MIMOWN as a generalization of the old architecture of wavelet network. This newel prototype was applied to speech recognition application especially to model acoustic unit of speech. The originality of our work is the proposal of MIMOWN to model acoustic unit of speech. This approach was proposed to overcome limitation of old wavelet network model. The use of the multi-input multi-output architecture will allows training wavelet network on various examples of acoustic units.