Volume 131, Issue 11 pp. 2567-2571
Laryngology

A Comparison of an Artificial Intelligence Tool to Fundamental Frequency as an Outcome Measure in People Seeking a More Feminine Voice

Yael Bensoussan MD, FRCSC

Corresponding Author

Yael Bensoussan MD, FRCSC

USC Voice Center, Caruso Department of Otolaryngology-Head and Neck Surgery, University of Southern California, Los Angeles, California, U.S.A.

Send correspondence to Yael Bensoussan, MD, FRCSC, University of Southern California Voice Center, Caruso Department of Otolaryngology-Head and Neck Surgery, 1540 San Pablo Street-90033, California, LA.

E-mail: [email protected]

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Christopher Park BA

Christopher Park BA

Grabscheid Voice and Swallowing Center, Department of Otolaryngology-Head and Neck Surgery, Mount Sinai Health System, New York, New York, U.S.A.

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Michael Johns III MD

Michael Johns III MD

USC Voice Center, Caruso Department of Otolaryngology-Head and Neck Surgery, University of Southern California, Los Angeles, California, U.S.A.

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Sarah Brown CCC-SLP

Sarah Brown CCC-SLP

Grabscheid Voice and Swallowing Center, Department of Otolaryngology-Head and Neck Surgery, Mount Sinai Health System, New York, New York, U.S.A.

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Jeremy Pinto MASc

Jeremy Pinto MASc

Department of Engineering, MILA Institute for Artificial Intelligence, Montreal, Ottawa, Canada

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Joseph Chang MD

Joseph Chang MD

Grabscheid Voice and Swallowing Center, Department of Otolaryngology-Head and Neck Surgery, Mount Sinai Health System, New York, New York, U.S.A.

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Mark Courey MD

Mark Courey MD

Grabscheid Voice and Swallowing Center, Department of Otolaryngology-Head and Neck Surgery, Mount Sinai Health System, New York, New York, U.S.A.

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First published: 11 May 2021
Citations: 1

Editor's Note: This Manuscript was accepted for publication on April 28, 2021

This study was accepted for oral presentation at the 142st Annual Meeting of the American Laryngological Association, online, April 7, 2020.

The authors have no funding, financial relationships, or conflicts of interest to disclose.

Abstract

Objectives/Hypothesis

An artificial intelligence (AI) tool was developed using audio clips of cis-male and cis-female voices based on spectral analysis to assess %probability of a voice being perceived as female (%Prob♀). This program was validated with 92% accuracy in cisgender speakers. The aim of the study was to assess the relationship of fo on %Prob♀ by a validated AI tool in a cohort of trans females who underwent intervention to feminize their voice with behavioral modification and/or surgery.

Study Design

Cohort study.

Methods

Fundamental frequency (fo) from prolonged vowel sounds (fo/a/) and fo from spontaneous speech (fo-sp) were measured using the Kay Pentax Computerized Speech Lab (Montvale, NJ) in trans females postintervention. The same voice samples were analyzed by the AI tool for %Prob♀. Chi-square analysis and regression models were performed accepting >50% Prob♀ as female voice.

Results

Forty-two patients were available for analysis after intervention. fo-sp post-treatment was positively correlated with %Prob♀ (R = 0.645 [P < .001]). Chi-square analysis showed a significant association between AI %Prob♀ >50% for the speech samples and fo-sp >160 Hz (P < .01). Sixteen of 42 patients reached an fo-sp >160 Hz. Of these, the AI program only perceived nine patients as female (>50 %Prob♀).

Conclusion

Patients with fo-sp >160 Hz after feminization treatments are not necessarily perceived as having a high probability of being female by a validated AI tool. AI may represent a useful outcome measurement tool for patients undergoing gender affirming voice care.

Level of Evidence

3 Laryngoscope, 131:2567–2571, 2021

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