Predicting hypernasality using spectrogram via Deep Convolutional Neural Network (DCNN)

Mohd Amir, Murni and Asnawi, Ani Liza and Zainal, Nur Aishah and Jusoh, Ahmad Zamani (2024) Predicting hypernasality using spectrogram via Deep Convolutional Neural Network (DCNN). In: 2024 IEEE International Conference on Computing, ICOCO 2024, 12-14 December 2024, Kuala Lumpur.

	PDF - Published Version Restricted to Registered users only Download (577kB) | Request a copy
Preview	PDF - Supplemental Material Download (151kB) | Preview

Abstract

Hypernasality, marked by excessive nasal resonance during a speech, impairs communication clarity, particularly for sounds requiring soft palate closure, such as the “f” in 'football'. Common causes include cleft lip and palate, velopharyngeal insufficiency, neurological disorders, adenoid issues, anatomical defects, and nerve system impairments. Detection and management in clinical settings are challenging due to the limited availability of speech-language pathologists (SLPs), subjective evaluation methods, and inefficient workflows. This study uses a speech feature with a deep learning model to predict the hypernasality in individuals. The objective of the study is to reduce reliance on SLPs, ensure consistent and objective evaluations, and enhance clinical efficiency. The study achieved a 92.31 accuracy in detecting hypernasality using the spectrogram features extraction method via Deep Convolutional Neural Network (DCNN). The clinical can apply this study by developing a user-friendly GUI and integrating it with a secure database. Using suitable tools for deployment, this study can be effectively applied in clinical settings. The contribution of this study is that it addresses the scarcity of SLPs and enhances clinical evaluation practices, ultimately improving communication outcomes for individuals with hypernasality. ©2024 IEEE.

Item Type:	Proceeding Paper (Other)
Additional Information:	Cited by: 0; Conference name: 2024 IEEE International Conference on Computing, ICOCO 2024; Conference date: 12 December 2024 through 14 December 2024; Conference code: 207836
Uncontrolled Keywords:	Deep neural networks; Neurophysiology; Ophthalmology; Spectrographs; Speech enhancement; Cleft lip and palates; Clinical settings; Convolutional neural network; Deep learning; Hypernasal; Neurological disorders; Soft palates; Spectrograms; Speech language pathologists; Subjective evaluations; Convolutional neural networks
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Fuziah Arifin
Date Deposited:	05 Jun 2025 12:39
Last Modified:	05 Jun 2025 12:39
URI:	http://irep.iium.edu.my/id/eprint/121352

Actions (login required)

View Item