Speech Stuttering Detection and Removal Using Deep Neural Networks

Shaswat Rajput, Ruban Nersisson, Alex Noel Joseph Raj, A. Mary Mekala, Olga Frolova, Elena Lyakso

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech in.wav format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model’s accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate (WER) for most of the test cases.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Computer Engineering and Networks
EditorsQi Liu, Xiaodong Liu, Bo Chen, Yiming Zhang, Jiansheng Peng
PublisherSpringer Nature
Pages443-451
Number of pages9
ISBN (Print)9789811665530
DOIs
StatePublished - 2022
Event11th International Conference on Computer Engineering and Networks, CENet2021 - Hechi, China
Duration: 21 Oct 202125 Oct 2021

Publication series

NameLecture Notes in Electrical Engineering
Volume808 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference11th International Conference on Computer Engineering and Networks, CENet2021
Country/TerritoryChina
CityHechi
Period21/10/2125/10/21

Scopus subject areas

  • Industrial and Manufacturing Engineering

Keywords

  • Classification
  • Deep learning
  • Filtering
  • Speech stuttering
  • Word Error Rate

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