Feature analysis for automatic detection of pathological speech

By A. Dibazar, Shrikanth S. Narayanan, T. Berger. 2002

148 citations recorded by the source corpus.

This study focuses on a robust, rapid and accurate system for automatic detection of normal and pathological speech. This system employs noninvasive, non-expensive and fully automated measures of vocal tract characteristics and excitation information. Mel-frequency filterbank cepstral coefficients and measures of pitch dynamics were modeled by Gaussian mixtures in a hidden Markov model (HMM) classifier. The method was evaluated using the sustained phoneme /a/ data obtained from over 700 subjects of normal and different pathological cases from the Massachusetts Eye and Ear Infirmary (MEEI) database. This method attained 99.44% correct classification rates for discrimination of normal and pathological speech for sustained /a/. This represents 8% detection error rate improvement over the best performing classifier using carefully measured features prevalent in the state-of-the-art in pathological speech analysis.

Subfields: Artificial Intelligence, Physiology, Signal Processing

Citations in the corpus

• Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? (2019, neutral): “…speech processing algorithms can infer not only the spoken contents from the speech signal, but also the speaker’s identity [5], intention [6–9], gender [10,11], emotional state [12–14], pathological condition [15–17], personality [18,19] and cultural [20,21] attributes to a great extent.”