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Towards interpretable speech biomarkers: exploring MFCCs

  • Brian Tracey
  • , Dmitri Volfson
  • , James Glass
  • , R’mani Haulcy
  • , Melissa Kostrzebski
  • , Jamie Adams
  • , Tairmae Kangarloo
  • , Amy Brodtmann
  • , E. Ray Dorsey
  • , Adam Vogel

Research output: Contribution to journalArticleResearchpeer-review

Abstract

While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.

Original languageEnglish
Article number22787
Number of pages9
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - Dec 2023

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