Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

Journal: Journal of voice : official journal of the Voice Foundation

PMID: 29567049

Abstract

OBJECTIVES: Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based approach to detect pathological voice and examines its performance and utility compared with other automatic classification algorithms.

Authors

Shih-Hau Fang

Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan.
Yu Tsao
Min-Jing Hsiao

Department of Electric Engineering, Yuan Ze University, Taoyuan, Taiwan.
Ji-Ying Chen

Department of Electric Engineering, Yuan Ze University, Taoyuan, Taiwan.
Ying-Hui Lai
Feng-Chuan Lin

Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, Taipei, Taiwan.
Chi-Te Wang

Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, Taipei, Taiwan.

Keywords

Acoustics Adult Aged Aged, 80 and over Deep Learning Diagnosis, Computer-Assisted Dysphonia Female Humans Male Middle Aged Pilot Projects Predictive Value of Tests Reproducibility of Results Retrospective Studies Signal Processing, Computer-Assisted Sound Spectrography Speech Acoustics Speech Production Measurement Support Vector Machine Vocal Cords Voice Quality Young Adult

External Resources

View on PubMed Access via DOI PubMed (29567049)

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

Abstract

Authors

Keywords

External Resources

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