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So what’s this project about?
TLDR: We sampled Gen Z males, had them record their voices reading a passage, and asked them to self-rate their sexuality on a 0-10 scale. We’re analyzing these recordings using advanced phonetic tools and plan to build neural networks that predict both self-reported sexuality and listener-perceived sexuality.
Click to expand a longer explanation.
This project explores the acoustic features that correlate with both self-reported sexuality and listener perceptions of sexuality in Gen Z male voices. We recruited Gen Z male participants who recorded themselves reading a passage (found below) and provided self-ratings of their sexuality on a scale from 0 (100% straight) to 10 (100% gay), with 5 being 50-50 bisexual.
The project is currently in the analysis phase, with plans to extend into perceptual studies in CUNY Queens where listeners will rate how "gay" the voices sound. This will give us two different prediction targets to train our neural networks on: self-reported sexuality scores and listener-perceived sexuality scores.
The Passage:
So this morning, I woke up late and immediately stubbed my toe on the dresser — great start, right? I grumbled something half-conscious, grabbed whatever shirt was clean-ish, and headed out. The sky looked weird, like that shade of blue-gray that makes you question if it's about to rain or not. I stopped by the corner store to grab a toasted bagel and orange juice, but they were out of both. The cashier joked, "It's Monday energy," and I half-laughed because, yeah, true. I took a seat near the window, scrolled my phone, and overheard someone say, 'The giraffe exhibit opens at noon,' which felt oddly profound at the time. After a few bites of something vaguely egg-shaped, I wandered to the park. A kid zoomed by on a scooter yelling, 'Zoom, zoom!' like it was a sound effect that summoned speed. I sat under a tree, listened to the breeze, and watched a yellow balloon get tangled in a power line. Weird morning. But honestly? Not the worst.
What makes this approach unique?
TLDR: We’re using a two-stage neural network approach: first, interpretable models using hand-crafted phonetic features (aka, they were previously reported in traditional studies), then high-capacity models on raw audio. This closes the gap between older, linguistic theory and modern AI/ML methods.
Click to expand a longer explanation.
Stage 1: Interpretable Models (Feature → Prediction)
We start with hand-crafted sociophonetic features including fundamental frequency (F0, aka pitch), formant frequencies (the resonant frequencies that give vowels their distinct sound), silibant center of gravity (how sharp or dull "s" sounds are), rhythmic patterns, and pausing behavior. These features are fed into transparent models like logistic regression (a statistical method that finds relationships between inputs and outputs), random forests, or linear SVMs. Using feature importance tools (methods that tell us which measurements matter most for predictions), we can identify which acoustic properties are significant predictors of sexuality perception.
This approach may not achieve state-of-the-art accuracy, but it provides crucial evidence that aligns with linguistic research expectations: "Pitch range and /s/ spectral characteristics are significant predictors of perceived sexuality."
Stage 2: High-Capacity Predictive Models
We then train deep neural networks (complex AI models with many layers) on raw waveforms (the actual audio signal) or self-supervised learning embeddings (pre-trained representations from models like Wav2Vec2, HuBERT, Whisper that have learned to understand speech). These models maximize predictive accuracy on held-out speakers and listeners (people not used during training). To understand what these black-box models (models that work well but don't explain their reasoning) are learning, we employ several probing techniques:
Stage 1.5: Targeted Feature Ablation
Once we identify which interpretable features matter most in Stage 1, we systematically remove them from the neural network's input. This process is called "ablation" - essentially controlled removal to test importance. For example, if /s/ center-of-gravity is important in our interpretable models, we mask sibilant regions (hide all the "s" and "sh" sounds) in the raw audio for Stage 2 models to see how prediction accuracy changes.
This bridging approach gives us both interpretable linguistic insights and high-performance predictive models, allowing us to say: "A black-box model predicts perceived sexuality at X% accuracy, and when we probe it, we find it relies heavily on prosodic features (rhythm, stress, intonation) and sibilants ("s" and "sh" sounds)."
What tools and methods are you using for acoustic analysis?
TLDR: Praat for phonetic annotation, Montreal Forced Aligner for phoneme alignment, and FastTrack for formant analysis. (Possibly more, will update.) This gives us more broad prosodic patterns and segmental details as well.
Click to expand a longer explanation.
Praat Phrase-Level Annotations
Praat is the gold standard for phonetic analysis in linguistics research. We use it to extract prosodic features like pitch contours, intensity patterns, and rhythm metrics across entire utterances. This captures the aspects of speech that often carry social meaning. We hand-made phrase-level annotations to feed into the Montreal Forced Aligner.
Montreal Forced Aligner (MFA)
MFA provides precise time alignments between the audio and phoneme sequences, allowing us to analyze specific sound segments. This is crucial for studying features like /s/ spectral characteristics, vowel formant patterns, and consonant-vowel timing relationships that have been implicated in previous research on sexuality perception. The phoneme-aligned textgrids are then fed into FastTrack.
FastTrack Formant Analysis
Professor Santiago Barrera's FastTrack repository offers formant tracking across different speakers and recording conditions. Formant patterns (especially F1 and F2) are key indicators of vowel quality and have been linked to perceived speaker characteristics in sociolinguistic research (especially in diphthongs for previous "gay voice" studies).
What’s next for this project?
TLDR: We’re currently analyzing the acoustic data and preparing to launch listener perception studies. The goal is to train two separate neural networks - one for self-reported scores and one for listener-rated scores - to understand the difference between identity and perception. If you’d like to test your gaydar, please do so by taking the survey here.
Click to expand a longer explanation.
Current Phase: Acoustic Analysis
We're getting comprehensive acoustic features from the recorded speech samples using our various tools. This includes fundamental frequency statistics, formant trajectories, spectral characteristics of fricatives and sibilants, rhythm and timing metrics, and voice quality measures.
Upcoming: Listener Perception Studies
We plan to have listeners rate how "gay" the voices sound, giving us a second prediction target alongside the self-reported sexuality scores. This will allow us to explore the potentially important distinction between sexual identity and how sexuality is perceived by others.
Dual Neural Network Approach
We'll train two separate neural networks: one trained on self-identified sexuality scores and another trained on listener-rated scores. Comparing these models will reveal whether the acoustic cues that predict self-identity are the same as those that inform listener perceptions, or if there are differences, for whatever reason if be.