Research Article

The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach

Volume: 17 Number: July, August, September 2026 August 21, 2026

The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach

Abstract

Purpose: Speech comprehension performance in noisy environments is a complex process that cannot be fully predicted by standard audiometric assessments. The aim of this study is to use machine learning (ML) algorithms to predict individuals’ difficulties in understanding speech in noise based on clinical data. Methods: The study utilized retrospective clinical data obtained from the Oldenburg Hearing Health Record (OHHR) dataset. Machine learning algorithms were trained to classify speech-in-noise performance based on frequencydependent hearing thresholds, cognitive status, and demographic factors, as assessed by the Digit Triple Test (DTT). Results: The trained models demonstrated high predictive accuracy in the classification task, with the highest performance achieved by logistic regression (LR) (AUC=0.952), gradient boosting (GB (AUC=0.951), and random forest (RF) (AUC=0.951). The analyses indicate that, despite the model being provided with cognitive data such as Vocabulary Size Test (VST), the algorithms derived their highest predictive power from pure-tone thresholds in the 1500–4000 Hz range. Conclusion: The findings confirm, based on the data, that the primary factor making it difficult to perceive speech in noise originates from the peripheral auditory system. With the high classification accuracy achieved (AUC> 0.95), standard hearing tests are transformed into a predictive clinical tool, providing a reliable digital decision-support mechanism for individuals who struggle with speech comprehension in noise.

Keywords

References

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Details

Primary Language

English

Subjects

Audiology

Journal Section

Research Article

Publication Date

August 21, 2026

Submission Date

May 5, 2026

Acceptance Date

June 21, 2026

Published in Issue

Year 2026 Volume: 17 Number: July, August, September 2026

APA
Gavgalı, Ö. (2026). The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach. Acıbadem Üniversitesi Sağlık Bilimleri Dergisi, 17(July, August, September 2026). https://doi.org/10.31067/acusaglik.1944495
AMA
1.Gavgalı Ö. The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach. Acibadem Univ Saglik Bilim Derg. 2026;17(July, August, September 2026). doi:10.31067/acusaglik.1944495
Chicago
Gavgalı, Özgenur. 2026. “The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach”. Acıbadem Üniversitesi Sağlık Bilimleri Dergisi 17 (July, August, September 2026). https://doi.org/10.31067/acusaglik.1944495.
EndNote
Gavgalı Ö (August 1, 2026) The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach. Acıbadem Üniversitesi Sağlık Bilimleri Dergisi 17 July, August, September 2026
IEEE
[1]Ö. Gavgalı, “The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach”, Acibadem Univ Saglik Bilim Derg, vol. 17, no. July, August, September 2026, Aug. 2026, doi: 10.31067/acusaglik.1944495.
ISNAD
Gavgalı, Özgenur. “The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach”. Acıbadem Üniversitesi Sağlık Bilimleri Dergisi 17/July, August, September 2026 (August 1, 2026). https://doi.org/10.31067/acusaglik.1944495.
JAMA
1.Gavgalı Ö. The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach. Acibadem Univ Saglik Bilim Derg. 2026;17. doi:10.31067/acusaglik.1944495.
MLA
Gavgalı, Özgenur. “The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach”. Acıbadem Üniversitesi Sağlık Bilimleri Dergisi, vol. 17, no. July, August, September 2026, Aug. 2026, doi:10.31067/acusaglik.1944495.
Vancouver
1.Özgenur Gavgalı. The Predictive Role of Specific Audiometric Frequencies in Speech-in-Noise Perception: A Machine Learning Approach. Acibadem Univ Saglik Bilim Derg. 2026 Aug. 1;17(July, August, September 2026). doi:10.31067/acusaglik.1944495