Exploring Shapely Values for Blood Glucose Level Prediction from Speech

Simone Pompe, Adria Mallol-Ragolta, Nicolas Schauer, Björn W. Schuller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We explore a novel dataset for blood glucose level prediction from self-recorded speech. The dataset contains 10 h 30 m 25 s of data from 63 German patients (44 f, 19 m). We model the paralinguistic information embedded in the voice, exploiting the Low-Level Descriptors (LLD) of the eGeMAPS feature set. We investigate the use of Shapely values to understand the contribution of each individual LLD on the inferences produced by a Support Vector Machine (SVM). We also compare the performance of subsets of the LLDs selected by the Shapely values, or transformed using Principal Component Analysis (PCA). We tackle the task as a 3-class classification problem with the Unweighted Average Recall (UAR) as the evaluation metric. The baseline SVM model scores a UAR of 51.8 % on the test partition. The best model selecting a subset of the LLDs based on the Shapely values obtains a UAR of 56.8 %, while the top model transforming the LLDs with PCA reaches a UAR of 42.0 %, both on the test partition.

Original languageEnglish
Title of host publicationSpeech Communication - 15th ITG Conference
PublisherVDE VERLAG GMBH
Pages81-85
Number of pages5
ISBN (Electronic)9783800761654
DOIs
StatePublished - 2023
Externally publishedYes
Event15th ITG Conference on Speech Communication - Aachen, Germany
Duration: 22 Sep 202324 Sep 2023

Publication series

NameSpeech Communication - 15th ITG Conference

Conference

Conference15th ITG Conference on Speech Communication
Country/TerritoryGermany
CityAachen
Period22/09/2324/09/23

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