Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors

IEEE Trans Biomed Eng. 2004 Feb;51(2):380-4. doi: 10.1109/TBME.2003.820386.


It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders--including glottic cancer--under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.

Publication types

  • Comparative Study
  • Evaluation Study
  • Validation Study

MeSH terms

  • Algorithms*
  • Cluster Analysis
  • Databases, Factual
  • Diagnosis, Computer-Assisted / methods*
  • Fourier Analysis
  • Humans
  • Nerve Net*
  • Pattern Recognition, Automated*
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity
  • Speech Acoustics*
  • Speech Production Measurement / methods*
  • Voice Disorders / classification*
  • Voice Disorders / diagnosis*
  • Voice Quality