Abstract
The paper presents a method for computer-aided detection of lateral sigmatism. The aim of the study is to design an automated sigmatism diagnosis tool. For that purpose, a reference speech corpus has been collected. It contains 438 recordings of a phoneme /s/ surrounded by certain vowels with normative and simulated pathological pronunciation. The acoustic signal is recorded with an acoustic mask, which is a set of microphones organised in a semi-cylindrical surface around the subject's face. Frames containing /s/ phoneme are subjected to beamforming and feature extraction. Two different feature vectors containing, e.g., Mel-frequency cepstral coefficients and fricative formants, are defined and evaluated in terms of binary classification involving support vector machines. A single-channel analysis is confronted with multi-channel processing. The experimental results show that the multi-channel speech signal processing supported by beamforming is able to increase the pathology detection capabilities in general.
| Original language | English |
|---|---|
| Pages (from-to) | 246-255 |
| Number of pages | 10 |
| Journal | Biocybernetics and Biomedical Engineering |
| Volume | 39 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
Keywords
- Automated acoustic analysis
- Computer-aided speech diagnosis
- Lateral sigmatism
- Multi-channel speech acquisition
ASJC Scopus subject areas
- Biomedical Engineering
Fingerprint
Dive into the research topics of 'Multi-channel acoustic analysis of phoneme /s/ mispronunciation for lateral sigmatism detection'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver