A Pipeline Consisting of Pattern Recognition and Finite Automata for Recognizing VCV Productions in the Study of Vocal Hyperfunction

Gbenga Omotara; Mark Berardi; Maria Dietrich; G. N. DeSouza

doi:10.1109/SSCI50451.2021.9659927

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Conference proceeding

A Pipeline Consisting of Pattern Recognition and Finite Automata for Recognizing VCV Productions in the Study of Vocal Hyperfunction

Gbenga Omotara, Mark Berardi, Maria Dietrich and G. N. DeSouza

2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp.1-7

12/05/2021

DOI: 10.1109/SSCI50451.2021.9659927

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Abstract

Relative fundamental frequency (RFF) is an acoustic measure used to quantify vocal effort in voice science. Since it seeks to capture transitions between (i.e. to/from) steady-state vowels and unvoiced consonants, any machine learning approach to recognize patterns in these transitions should require time properties capable of identifying the sequence of phonemes. At the same time, Neural Networks (NN) have become a ubiquitous solution for data-driven problems, and Recursive NNs (RNN) provide a time-series schema to address time-dependent problems. Indeed, typical Neural Network solutions require either a time-series schema like in RNN or some spectral transformation to be able to handle time-dependent data. In this study, we decided to ignore - at least momentarily - any time-series dependency of the data and employed a simple NN to classify elements of the speech. Later, a State-Machine was used to identify their sequence with the purpose of localizing the transitions between voiced and unvoiced sounds in vowel-consonant-vowel (VCV) productions. The goal of this study was to demonstrate that a pipeline consisting of time-agnostic (Neural Network) and time-dependent (State Machine) components can be used to recognize time-dependent patterns in VCV productions.

Machine Learning

Pattern Recognition

Artificial neural networks

Manuals

Neural Networks

Pipelines

Production

Recurrent neural networks

Recursive NN

RFF

time-series

vocal hyperfunction

Details

Title: Subtitle: A Pipeline Consisting of Pattern Recognition and Finite Automata for Recognizing VCV Productions in the Study of Vocal Hyperfunction
Creators: Gbenga Omotara - University of Missouri
Mark Berardi - University Hospital Bonn
Maria Dietrich - University Hospital Bonn
G. N. DeSouza - University of Missouri
Resource Type: Conference proceeding
Publication Details: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp.1-7
Publisher: IEEE
DOI: 10.1109/SSCI50451.2021.9659927
Number of pages: 7
Language: English
Date published: 12/05/2021
Academic Unit: Communication Sciences and Disorders
Record Identifier: 9984721126202771

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