Abstract

The study of speech pathology involves evaluation and treatment of speech production related disorders affecting phonation, fluency, intonation and aeromechanical components of respiration. Recently, speech pathology has garnered special interest amongst machine learning and signal processing (ML-SP) scientists. This growth in interest is led by advances in novel data collection technology, data science, speech processing and computational modeling. These in turn have enabled scientists in better understanding both the causes and effects of pathological speech conditions. In this paper, we review the application of machine learning and signal processing techniques to speech pathology and specifically focus on three different aspects. First, we list challenges such as controlling subjectivity in pathological speech assessments and patient variability in the application of ML-SP tools to the domain. Second …

Date

2016

Authors

Rahul Gupta, Theodora Chaspari, Jangwon Kim, Naveen Kumar, Daniel Bone, Shrikanth Narayanan

Conference

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

6470-6474

Publisher

IEEE