HypernasalityNet: Deep recurrent neural network for automatic hypernasality detection.

Journal: International journal of medical informatics
Published Date: Sep 1, 2019

Abstract

BACKGROUND: Cleft palate patients have inability to produce adequate velopharyngeal closure, which results in hypernasal speech. In clinic, hypernasal speech is assessed through subject assessment by speech language pathologists. Automatic hypernasal speech detection can provide aided diagnoses for speech language pathologists and clinicians.

Authors

  • Xiyue Wang
    College of Electrical Engineering and Information Technology, Sichuan University, 610065, China. Electronic address: xiyue.wang.scu@gmail.com.
  • Sen Yang
    Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
  • Ming Tang
    Business School, Sichuan University, Chengdu 610064, China. tangming0716@163.com.
  • Heng Yin
    Liaoning Provincial Key Laboratory of Carbohydrates, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China. yinheng@dicp.ac.cn.
  • Hua Huang
    Department of Radiology, National Cancer Center/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
  • Ling He
    Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

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