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Learning diagnostic models using speech and language measures.

Bart Peintner1, William Jarrold, Dimitra Vergyriy

  • 1SRI International, Menlo Park, CA, USA. peintner@ai.sri.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed
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Machine learning effectively diagnoses neurodegenerative diseases impacting speech. Models analyzing audio and language features predict Frontotemporal Lobar Degeneration subtypes with high accuracy.

Area of Science:

  • Computational neuroscience
  • Artificial intelligence in medicine
  • Speech-language pathology

Background:

  • Neurodegenerative diseases often affect speech and language production.
  • Accurate diagnosis of subtypes like Frontotemporal Lobar Degeneration (FTLD) is crucial for effective treatment.
  • Current diagnostic methods can be invasive or time-consuming.

Purpose of the Study:

  • To investigate the effectiveness of machine learning (ML) for the automatic diagnosis of FTLD subtypes.
  • To determine if ML models can accurately predict FTLD diagnosis based on speech and language features.

Main Methods:

  • Collected audio recordings from 9 healthy controls and 30 patients with diagnosed FTLD subtypes.
  • Extracted acoustic and linguistic features from the audio data using automated transcription.

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  • Developed and trained ML models to predict FTLD diagnosis using the extracted features.

    • Main Results:

    • The developed ML models demonstrated predictive accuracy significantly better than random chance.
    • Specific audio and linguistic features were identified as important predictors for FTLD subtypes.
    • The study highlights the potential of ML in objective disease diagnosis.

    Conclusions:

    • Machine learning shows significant promise for the early and accurate diagnosis of neurodegenerative diseases affecting speech.
    • Automated analysis of speech and language provides a non-invasive tool for FTLD subtype identification.
    • Further research with higher quality recordings may enhance diagnostic performance.