Skip to main content

Speech reconstruction using a deep partially supervised neural network

McLoughlin, Ian Vince, Li, Jingjie, Song, Yan, Sharifzadeh, Hamid Reza (2017) Speech reconstruction using a deep partially supervised neural network. IET Healthcare Technology Letters, 4 (4). pp. 129-133. ISSN 2053-3713. E-ISSN 2053-3713. (doi:10.1049/htl.2016.0103)

PDF - Author's Accepted Manuscript

Creative Commons Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Download (504kB) Preview
Official URL


Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays, however deep neural network-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. We propose a novel deep neural network structure that allows a partially supervised training approach on spectral features from smaller datasets, yielding very good results compared to the current state-of-the-art.

Item Type: Article
DOI/Identification number: 10.1049/htl.2016.0103
Uncontrolled keywords: Speech reconstruction, post-laryngectomy speech, statistical voice conversion
Subjects: T Technology > T Technology (General)
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Ian McLoughlin
Date Deposited: 21 Apr 2017 09:18 UTC
Last Modified: 09 Jul 2019 11:19 UTC
Resource URI: (The current URI for this page, for reference purposes)
McLoughlin, Ian Vince:
  • Depositors only (login required):


Downloads per month over past year