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Deep Learning for Voice Gender Identification: Proof-of-concept for Gender-Affirming Voice Care.

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This study developed an AI model using neural networks to accurately predict gender from voice samples. This technology shows promise for objectively measuring outcomes in gender-affirming voice care.

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Area of Science:

  • Artificial Intelligence
  • Speech Science
  • Transgender Health

Background:

  • Gender-affirming voice care is increasingly needed by the transgender population.
  • Objective measures to assess treatment success in voice care are currently lacking.

Purpose of the Study:

  • To develop a proof-of-concept for an AI-assisted tool to measure treatment outcomes in gender-affirming voice care.
  • To use neural network models to predict binary gender from audio samples.

Main Methods:

  • A deep neural network was trained on 278 male and female voices from the Perceptual Voice Qualities Database.
  • Audio samples were converted to Mel spectrograms for frequency domain analysis.
  • 10-fold cross-validation was employed with an 80% training, 10% validation, and 10% test split.

Main Results:

  • The AI model achieved an overall accuracy of 92% in predicting gender from voice.
  • The model demonstrated higher accuracy for female voices (F1 score 0.94) than male voices (F1 score 0.87).

Conclusions:

  • This preliminary study demonstrates the potential of AI for objective outcome measurement in gender-affirming voice care.
  • Further development could lead to an AI-assisted tool for evaluating voice care interventions.