Adjusting Speaker Age and Gender in Custom TTS Models

Parametric adjustment of vocal age and gender in custom Text-to-Speech (TTS) models allows developers to synthesize diverse vocal profiles from limited training data. This process relies on modifying core acoustic features, such as fundamental frequency and vocal tract dimensions, as well as manipulating deep latent representations within neural architectures. By leveraging techniques such as explicit conditioning vectors, latent space disentanglement, and neural acoustic modeling, systems can dynamically shift perceived speaker age and gender along continuous spectrums without degrading speech naturalness.

Fundamental Acoustic Determinants of Age and Gender

Vocal age and gender perception depends primarily on biological acoustic markers that dictate how sound is generated by the vocal cords and filtered by the vocal tract:

• Fundamental Frequency (\(F_0\)): \(F_0\) determines perceived vocal pitch. Adult biological females typically exhibit an average \(F_0\) between 165 and 255 Hz, whereas adult biological males average between 85 and 155 Hz. Children exhibit significantly higher \(F_0\) values (often exceeding 250–300 Hz). In older adults, physiological changes often cause male pitch to rise slightly and female pitch to drop.
• Formant Frequencies and Vocal Tract Length: The length and geometry of the vocal tract dictate resonance points known as formants (\(F_1, F_2, F_3\)). Longer vocal tracts (typical of adult males) compress formant spacing toward lower frequencies, while shorter vocal tracts (typical of females and children) shift formants upward.
• Aspiration, Glottal Waveform, and Spectral Tilt: Aging voices typically introduce higher jitter (pitch instability), shimmer (amplitude instability), reduced harmonics-to-noise ratio (HNR), and increased breathiness due to incomplete vocal fold closure.

Continuous Conditioning with Attribute Embeddings

Modern neural TTS architectures (such as FastSpeech 2, VITS, or diffusion-based generators) model speaker variation by conditioning the acoustic backbone on multi-dimensional embeddings:

1. Scalar and Vector Conditioning: Alongside global speaker embeddings (such as x-vectors or d-vectors), explicit numerical conditioning layers for target age (e.g., normalized float values from 0.0 to 1.0) and gender can be passed directly to variance adaptors and duration predictors.
2. Joint Feature Injection: These attribute embeddings are concatenated with the phoneme representations and speaker representations, allowing the model's cross-attention mechanisms or convolutional blocks to map specific phonemes to target pitch, duration, and energy profiles corresponding to the desired demographics.

Latent Space Disentanglement

When training custom models on multi-speaker datasets, age and gender are frequently entangled with speaker identity, accent, and recording acoustics. To achieve precise parametric control, systems utilize disentangled representation learning:

• Variational Autoencoders (VAEs): VAEs allow acoustic parameters to be mapped to a structured latent space. By applying supervised penalties or mutual information minimization, a specific latent sub-space can be constrained to encode age and gender independently from linguistic content and individual timbre.
• Adversarial Training: Gradient reversal layers or auxiliary discriminators are applied during training to penalize the acoustic encoder if speaker identity can predict age/gender, or vice versa. This forces the model to treat age and gender as independent, steerable vectors. Moving along these learned vector paths allows seamless interpolation between ages (e.g., child to adult to senior) or across gender characteristics.

Direct Parametric and DSP Adjustments

In production pipelines where retraining neural weights is impractical, parametric transformation can be applied during mel-spectrogram generation or prior to the vocoder phase:

• Vocal Tract Length Normalization (VTLN): VTLN applies frequency warping factors (\(\alpha\)) directly to linear or mel-scale filter banks. Setting \(\alpha > 1.0\) simulates vocal tract compression (sounding younger or more feminine), whereas \(\alpha < 1.0\) simulates tract elongation.
• \(F_0\) Scaling and Variance Modification: Directly modifying the mean and dynamic range of the predicted pitch contour alters vocal identity. Applying dynamic range reduction combined with lower base \(F_0\) produces older male characteristics, while expanding variance and elevating \(F_0\) shifts the voice toward youth.
• Aperiodic Energy Manipulation: Injecting controlled noise into the higher-frequency bands of the neural vocoder input (such as HiFi-GAN or BigVGAN) reproduces the breathiness, vocal fatigue, and hoarseness associated with advanced vocal aging.

Diffusion and Flow-Matching Trajectory Guidance

In modern score-based and flow-matching TTS architectures, parametric age and gender adjustments are executed via classifier-free guidance. By tuning the conditioning scale during the reverse-diffusion sampling loop, practitioners can amplify or suppress specific demographic cues without destabilizing phoneme alignment, yielding fine-grained control over the final vocal identity.