Gender-specific spectral features

Updated 1 July 2025

Gender-specific spectral features are frequency-domain properties of signals (like speech, EEG, images) that differ systematically between genders due to physiological variations.
These features, extracted using methods like MFCCs or power spectral density, are crucial inputs for machine learning models in applications such as speech recognition, biometrics, and neuroimaging.
Effective use depends on context (e.g., specific phonemes in speech, brain regions in EEG), highlighting the need for feature selection and awareness of potential biases for fair system design.

Gender-specific spectral features refer to measurable differences in the spectral (frequency-domain) properties of signals—such as speech, EEG, or images—that systematically vary according to gender, serving as discriminative cues in classification tasks. These features are typically extracted as part of feature engineering pipelines for applications like gender recognition in speech, face, neuroimaging, and behavior sensing, and can be exploited directly by machine learning models or indirectly via deep representation learning.

1. Theoretical Foundations and Definitions

Gender-specific spectral features are grounded in physiological and anatomical differences. In speech, these are rooted in average differences in fundamental frequency (F0), vocal tract length, and formant structure between male and female speakers. In EEG, they arise from neural and cognitive processing differences reflected as distinct patterns in power spectral density or event-related potentials (ERPs). In images, spectral features may derive from reflectance or texture differences captured in specific frequency bands.

In the case of speech, the mathematical basis for many spectral features used for gender discrimination is the transformation of the time-domain signal into a frequency representation, typically using the Short-Time Fourier Transform (STFT) and Mel filterbanks, yielding Mel-Frequency Cepstral Coefficients (MFCCs):

$C_k = \sum_{j=1}^{N} \log(m_j) \cos\left[ \frac{\pi k}{N} (j - 0.5) \right]$

where $m_j$ are the Mel-scaled filterbank energies, and $N$ is the number of filters (1601.01577).

In EEG, gender-specific spectral features commonly refer to differences in relative band power or temporal ERPs, such as amplitude and latency of N100, P300, or N400 components at specific electrodes and conditions (1708.08735, 2006.13386, 2303.06376).

2. Extraction Methodologies Across Modalities

Speech Signals

MFCCs: Widely used as descriptors; encapsulate gender-dependent differences in both fundamental frequency and formant structure (1601.01577). Extraction involves framing, windowing, FFT, Mel-filterbank application, log-energy calculation, and DCT decorrelation.
Spectral Contrast & Melspectrogram: Capture dynamic range and time-frequency energy distribution, further discriminating gender-specific speech dynamics (2112.09596).
Statistical Summaries: Mean, variance, skewness, and higher-order statistics of spectral features over the utterance or segment; key for robust short-utterance classification.

Images (VIS/NIR)

Texture and Shape: Coded using Uniform Local Binary Patterns (ULBP) and Histogram of Oriented Gradients (HOG); periocular NIR images, with informative features found primarily in the peri-iris region rather than the iris proper (1904.12007).
Spectral Bands: Differences in reflectance (visible vs. near-infrared) are leveraged by deep autoencoders or SVMs; salient features are learned or selected based on classification relevance (1805.07905).

EEG and Eye Movement

EEG Spectral Features: Power spectral density estimates via Welch’s method, summarized for canonical frequency bands (theta, alpha, beta), or computed as relative power per channel/band (2105.04762, 2303.06376).
ERPs: Amplitude and latency of time-locked components observed at specific channels, notably N100, P300, and N400 in response to emotion tasks; qualitative and quantitative gender separation is apparent, e.g., higher female ERP amplitudes for negative emotions (1708.08735, 2006.13386).
Eye fixations: Fixation distributions, particularly for face regions under occlusion, where gender-specific strategies in visual scanning emerge (2006.13386).

3. Empirical Discriminability and Classification Performance

Gender-specific spectral features have consistently been shown to enable accurate classification in a variety of applied tasks:

Speech: SVMs trained on MFCC features achieved up to 90.1% accuracy on telephone speech, outperforming k-nearest neighbor, naive Bayes, MLP, and random forest (1601.01577). In low-resolution, multi-spectral face classification, AutoGen’s supervised autoencoder achieved 90.10% (VIS) and 71.32% (NIR) accuracy at 24×24 pixels (1805.07905).
EEG: Single-channel gender AUCs often exceeded 0.8, and convolutional neural networks using raw EEG achieved up to 0.931 AUC in emotional tasks (2006.13386). Considerable EEG-based gender separation was observed, particularly for negative emotion processing (1708.08735, 2006.13386).
Periocular Imaging: Feature selection within the peri-iris region (excluding the iris) optimizes SVM classification up to 89.22% in NIR images (1904.12007).
Children’s Speech: Hierarchical clustering and random forest modeling showed varying key features by age, with F0/VT-length features dominating post-puberty; overall average F1 score was 0.84 with higher accuracy for older age groups (2209.13112).

4. Modality-Specific Characteristics and Limitations

Spectral gender markers are context-dependent:

Speech: Gender differences are pronounced in voiced phoneme spectra (vowels, voiced consonants), but are minimal or functionally absent in unvoiced segments (fricatives, stops, silences). Replacement of unvoiced segments with opposite-gender counterparts goes undetected perceptually and causes only slight degradations in automatic phoneme recognition (1807.05813).
Periocular Images: The surrounding peri-iris region is more informative than the iris for gender discrimination, contradicting earlier assumptions; data-driven feature selection optimizes speed and accuracy (1904.12007).
EEG for Clinical Inference: In Parkinson’s disease detection, relative power in frontal and parietal channels in theta/beta/alpha bands was higher in males, explaining classifier disparities (e.g., 80.5% vs 63.7% accuracy for PD detection in males vs females) (2303.06376).
Breath Sounds: Even non-phonated segments (breath) yield weak but usable gender cues; MFCC statistics from 3s breath segments suffice for simple classifiers (2211.06371).

5. Practical Applications and System Design

Practical systems deploy gender-specific spectral features as a core input in domains including:

Telephony: Gender recognition optimizes downstream speech processing (ASR, IVR), with SVMs on MFCCs handling brief utterances robustly (1601.01577).
Surveillance and Biometrics: Low-resolution and near-IR spectra are exploited for gender detection in security/retail; autoencoders deliver compact, discriminative representations (1805.07905).
Emotion Recognition: CNNs using MFCC, melspectrogram, and spectral contrast features as input, with stratified performance assessment across genders and languages (2112.09596).
EEG Profiling: For emotion or disease recognition, gender-aware modeling prevents disparities and is essential for clinical fairness (2006.13386, 2303.06376).
Speech Enhancement Evaluation: Phoneme-level, gender-aware analyses reveal hidden performance differences; female speech (plosives, fricatives, vowels) benefits more from current enhancement algorithms, especially at higher SNRs, showing lower artifacts and higher intelligibility (2506.18691).

Application Domain	Dominant Spectral Features	Best-performing Modality/Model
Telephony speech recognition	MFCCs, mean vectors	SVM (poly/RBF kernel)
Multispectral face analysis	Autoencoded feature embeddings	AutoGen neural net
EEG-based gender/emotion	ERP peaks, PSD in θ/β bands	CNNs, SVM, Adaboost (eye features)
NIR periocular imaging	HOG, ULBP, intensity (peri-iris)	SVM (Gaussian kernel), XgBoost

6. Emerging Topics, Challenges, and Recommendations

Analyses across multiple modalities reveal recurring themes:

Context-dependence: Spectral gender cues are highly dependent on phoneme type (speech), region (peri-iris vs. iris), or brain state (EEG); generic systems must account for these factors (1807.05813, 2506.18691).
Feature Selection: Targeted, relevance-based selection (e.g., XgBoost Gini index) enhances classification accuracies while reducing computational burden (1904.12007).
Fairness and Bias: Disparities arise if models are not explicitly gender-aware in medical or biometric settings; separate or balanced training, and careful subgroup analysis, are necessary (2303.06376).
Interpretability: Models that minimize intra-class variation and maximize inter-class variation (e.g., AutoGen) produce more transparent and robust gender-specific representations (1805.07905).
System Efficiency: Excluding uninformative regions (iris) or reducing features to those most relevant enables faster, more resource-efficient deployments (1904.12007).

7. Summary and Future Directions

Gender-specific spectral features are central to a range of classification tasks across speech, image, and neural domains. Their effective extraction and exploitation hinge upon understanding physiological bases, context, and the interplay with phoneme or task-specific factors. Advanced machine learning approaches—including SVMs, autoencoders, CNNs, and ensemble methods—have demonstrated high efficacy when grounded in focused spectral feature sets. Ongoing research emphasizes the importance of feature selection, context-aware analysis, and fairness, particularly in clinical, biometric, and human-computer interaction applications. As research progresses, integration of domain knowledge and data-driven methods is expected to yield increasingly fine-grained and contextually robust systems for gender recognition and related behavioral or biometric inferences.