Towards a Deep Learning Based ASR System for Users with Dysarthria

  • Davide MulfariEmail author
  • Gabriele Meoni
  • Marco Marini
  • Luca Fanucci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10896)


In this paper, we investigate the benefits of deep learning approaches for the development of personalized assistive technology solutions for users with dysarthria, a speech disorder that leads to low intelligibility of users’ speaking. It prevents these people from using automatic speech recognition (ASR) solutions on computers and mobile devices. In order to address these issue, our effort is to leverage convolutional neural networks toward a speaker dependent ASR software solution intended for users with dysarthria, which can be trained according to particular user’s needs and preferences.


Deep learning Assistive technology Speech recognition Dysarthria 


  1. 1.
    Joy, N.M., Umesh, S.: Improving acoustic models in TORGO Dysarthric speech database. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 637–645 (2018)CrossRefGoogle Scholar
  2. 2.
    Polur, P.D., Miller, G.E.: Effect of high-frequency spectral components in computer recognition of dysarthric speech based on a mel-cepstral stochastic model. J. Rehabil. Res. Dev. 42(3), 363 (2005)CrossRefGoogle Scholar
  3. 3.
    Sainath, T.N., Parada, C.: Convolutional neural networks for small-footprint keyword spotting. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)Google Scholar
  4. 4.
    Tejaswi, S., Umesh, S.: DNN acoustic models for Dysarthric speech. In: 2017 Twenty-Third National Conference on Communications (NCC), pp. 1–4. IEEE (2017)Google Scholar
  5. 5.
    Young, V., Mihailidis, A.: Difficulties in automatic speech recognition of dysarthric speakers and implications for speech-based applications used by the elderly: a literature review. Assist. Technol. 22(2), 99–112 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Davide Mulfari
    • 1
    Email author
  • Gabriele Meoni
    • 1
  • Marco Marini
    • 1
  • Luca Fanucci
    • 1
  1. 1.University of PisaPisaItaly

Personalised recommendations