26dgpp19 10.3205/26dgpp19 urn:nbn:de:0183-26dgpp195 Vortrag Patel Patel P. P

University Hospital Erlangen, Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology Erlangen, Head and Neck Surgery, Waldstraße 1, 91054 Erlangen, DeutschlandUniversity Hospital Erlangen, Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology Erlangen, Head and Neck Surgery, Erlangen, Germany

param.patel@uk-erlangen.de author Donhauser Donhauser J. J

University Hospital Erlangen, Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology Erlangen, Head and Neck Surgery, Erlangen, Germany

author Schützenberger Schützenberger A. A

University Hospital Erlangen, Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology Erlangen, Head and Neck Surgery, Erlangen, Germany

author Kunduk Kunduk M. M

Louisiana State University, Department of Communication Sciences and Disorders, Louisiana, United States

author Döllinger Döllinger M. M

University Hospital Erlangen, Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology Erlangen, Head and Neck Surgery, Erlangen, Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 20260826 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0654 19 Deutsche Gesellschaft für Phoniatrie und Pädaudiologie 42. Wissenschaftliche Jahrestagung der Deutschen Gesellschaft für Phoniatrie und Pädaudiologie (DGPP) Stimme: Stimmanalyse II Starnberg 20260916 20260919 V16 Background: Hoarseness caused by different voice disorders may exhibit different acoustic signatures, complicating standardized quantitative assessment. Acoustic voice signals recorded during sustained phonation are commonly used for objective assessment of voice quality. Laryngeal high-speed videoendoscopy (HSV) enables simultaneous recording of acoustic signals during phonation. However, the presence of rigid endoscope in the oral cavity results in acoustic signals that may differ from natural voice production. Hence, the goals of this study are to determine an optimal, as short as possible, analysis time interval and analyze the potential of HSV-synchronized acoustic signals for machine learning (ML)-based hoarseness severity estimation.Materials and methods: Two databases containing normal voices, functional and organic voice disorders were constructed. Database D1 comprises 824 HSV-synchronized acoustic recordings of sustained vowel /i/, while Database D2 includes 804 sustained vowel /a/ recordings from speech therapy sessions. Recording segments of 250 ms, 500 ms, and 1000 ms were analyzed. RBH ratings derived from continuous speech served as ground truth. Subjects were categorized into two hoarseness levels (H < 2 vs. H ≥ 2), while ML-based estimated probability was interpreted as a continuous interval-scaled severity score between 0 and 1. Comprehensive acoustic features were extracted and reduced using ensemble feature selection. ML models of varying complexity (logistic regression, SVM, XGBoost, TabNet) were evaluated across durations.Results: At 1000 ms, Logistic regression for D1 and XGBoost for D2 achieved the best performance, yielding Spearman rank correlations of 0.62 and 0.75 respectively between predicted severity scores and perceptual ratings. Across models, D2 demonstrated consistently higher performance than D1, with mean differences of approximately 10% in accuracy and 8% in ROC-AUC. Conclusion: The lower performance of HSV-synchronized acoustic recordings is likely related to the background noise from the equipment, which may degrade signal quality. Perceptual uncertainty in the ground truth (H = 1 vs. H = 2) may further influence model predictions. HSV-synchronized recordings show potential for reliable hoarseness severity estimation even under phonation constraints, with a 500 ms analysis interval sufficient for clinical assessment of voice quality. TextBackgroundHoarseness caused by different voice disorders may exhibit different acoustic signatures, complicating standardized quantitative assessment. Acoustic voice signals recorded during sustained phonation are commonly used for objective assessment of voice quality . Laryngeal high-speed videoendoscopy (HSV) enables simultaneous recording of acoustic signals during phonation. However, the presence of a rigid endoscope in the oral cavity results in acoustic signals that may differ from natural voice production . Hence, the goals of this study are to determine an optimal, as short as possible, analysis time interval and analyze the potential of HSV-synchronized acoustic signals for machine learning (ML)-based hoarseness severity estimation.Materials and methodsTwo databases containing normal voices, functional and organic voice disorders were constructed. Database D1 comprises 824 HSV-synchronized acoustic recordings of sustained vowel /i/, while Database D2 includes 804 sustained vowel /a/ recordings from speech therapy sessions. Recording segments of 250 ms, 500 ms, and 1000 ms were analyzed. RBH ratings derived from continuous speech served as ground truth . Subjects were categorized into two hoarseness levels (H < 2 vs. H ≥ 2), while ML-based estimated probability was interpreted as a continuous interval-scaled severity score between 0 and 1. Comprehensive acoustic features were extracted and reduced using ensemble feature selection. ML models of varying complexity (logistic regression, SVM, XGBoost, TabNet) were evaluated across durations. ResultsAt 1000 ms, Logistic regression for D1 and XGBoost for D2 achieved the best performance, yielding Spearman rank correlations of 0.62 and 0.75 respectively between predicted severity scores and perceptual ratings. Across models, D2 demonstrated consistently higher performance than D1, with mean differences of approximately 10% in accuracy and 8% in ROC-AUC. Figure 1 , Figure 2 ConclusionThe lower performance of HSV-synchronized acoustic recordings is likely related to the background noise from the equipment, which may degrade signal quality. Perceptual uncertainty in the ground truth (H = 1 vs. H = 2) may further influence model predictions. Despite these limitations, HSV-synchronized recordings show potential for reliable hoarseness severity estimation even under phonation constraints, with a 500 ms analysis interval sufficient for clinical assessment of voice quality. Dejonckere PH Bradley P Clemente P Cornut G Crevier-Buchman L Friedrich G Van De Heyning P Remacle M Woisard V Committee on Phoniatrics of the European Laryngological Society (ELS) A basic protocol for functional assessment of voice pathology, especially for investigating the efficacy of (phonosurgical) treatments and evaluating new assessment techniques. Guideline elaborated by the Committee on Phoniatrics of the European Laryngological Society (ELS) 2001 Eur Arch Otorhinolaryngol 77-82 Dejonckere PH, Bradley P, Clemente P, Cornut G, Crevier-Buchman L, Friedrich G, Van De Heyning P, Remacle M, Woisard V; Committee on Phoniatrics of the European Laryngological Society (ELS). A basic protocol for functional assessment of voice pathology, especially for investigating the efficacy of (phonosurgical) treatments and evaluating new assessment techniques. Guideline elaborated by the Committee on Phoniatrics of the European Laryngological Society (ELS). Eur Arch Otorhinolaryngol. 2001 Feb;258(2):77-82. DOI: 10.1007/s004050000299 https://doi.org/10.1007/s004050000299 Ng ML Bailey RL Acoustic changes related to laryngeal examination with a rigid telescope 2006 Folia Phoniatr Logop 353-62 Ng ML, Bailey RL. Acoustic changes related to laryngeal examination with a rigid telescope. Folia Phoniatr Logop. 2006;58(5):353-62. DOI: 10.1159/000094569 https://doi.org/10.1159/000094569 0

1 1 Figure 1: Model predicted probability scores stratified by ground truth hoarseness ratings H for databases D1 (Logistic regression) and D2 (XGBoost), with Spearman rank correlation (ρ) indicated. A regression line is fitted over predictions.

2 2 Figure 2: ROC-AUC score on test sets for D1 and D2 across analysis intervals. Shaded bands represent the standard error.

2 0 0