Voice Impression Control Method

• Voice impression control is a method that defines and manipulates high-level voice attributes using interpretable, low-dimensional impression vectors.
• It employs adversarial learning to disentangle speaker identity from perceptual qualities, ensuring independent and precise control over voice characteristics.
• LLMs translate natural language descriptions into impression vectors, enabling flexible, zero-shot TTS applications and customizable voice synthesis.

Voice impression control is the field concerned with the precise manipulation, synthesis, and evaluation of speech to elicit targeted high-level perceptual qualities—such as “brightness,” “calmness,” “masculinity,” expressivity, or specific styles—that shape how listeners experience a voice. It extends beyond conventional speaker identity or prosody transfer to encompass subjective and nuanced aspects of voice production, and has become a central research area in controllable speech synthesis, zero-shot voice conversion, and explainable voice manipulation.

1. Mathematical and Representational Foundations

Recent advances define voice impression along interpretable, low-dimensional axes—each representing a perceptual quality or antonymic pair (e.g., dark–bright, high–low pitch, masculine–feminine). In modern frameworks, voice impression control is typically achieved by introducing an impression vector $\mathbf{v} \in \mathbb{R}^n$, where each entry $v_i$ quantifies the location along the $i$-th perceptual dimension. Commonly used impression pairs (for $n=11$) include:

• High–Low pitched
• Masculine–Feminine
• Clear–Hoarse
• Calm–Restless
• Powerful–Weak
• Youthful–Elderly
• Thick–Thin
• Tense–Relaxed
• Dark–Bright
• Cold–Warm
• Slow–Fast

Each $v_i$ is typically scored on a discrete Likert scale (e.g., $1\sim7$), enabling granular control:

$\mathbf{v} = [v_1, v_2, \dots, v_{11}]$

The impression control module then injects $\mathbf{v}$ as a conditioning vector in the synthesis network, separate from the speaker identity embedding. This allows independent and explicit control over nuanced para- and non-linguistic characteristics of the synthesized speech.

Disentanglement of speaker identity (encapsulated by SSL or pretrained speaker encoders) from impression vectors is enforced through adversarial learning techniques such as gradient reversal layers and dropout, ensuring that identity and impression controls act orthogonally.

2. LLM-Based Impression Specification

One barrier in high-dimensional impression spaces is complexity of manual configuration. To address usability and accessibility, state-of-the-art approaches utilize LLMs that can map natural language descriptions to appropriate target impression vectors. The workflow:

• The user provides a free-form prompt (e.g., "Generate a sleepy yet urgent female voice").
• The LLM, prompted with descriptions of each impression-pair scale, translates the textual instruction into the corresponding $\mathbf{v}$.
• This vector can be further fine-tuned manually if required.

This LLM-based impression mapping enables non-technical users to specify and manipulate impression controls without resorting to trial-and-error with numerical vectors, thereby broadening the applicability in real-world scenarios.

3. Integration with Zero-Shot TTS Architectures

In a typical zero-shot text-to-speech (TTS) system with impression control, the architecture comprises:

• Speaker Encoder: Extracts a representation $\mathbf{x}$ of the speaker identity from a reference utterance, regularized to be impression-agnostic.
• Impression Control Module: Projects both the impressionless speaker embedding ($\mathbf{x}$, after adversarial removal of impression information) and the impression vector ($\mathbf{v}$) to lower-dimensional forms (e.g., 32D each), concatenates or combines them, and injects the result as conditions into the synthesis decoder.
• Backbone TTS Model: An established architecture (such as FastSpeech 2) synthesizes speech from text, conditioned on both content features and the combined (identity, impression) latent vector.
• Vocoder: Generates waveforms from predicted mel-spectrograms.

The impression control module is typically trained post hoc with the backbone TTS model kept frozen, optimizing for both impression alignment (MSE/GAN losses) and speaker identity preservation.

4. Objective and Subjective Evaluation Protocols

Effectiveness of impression control is validated using both objective and subjective protocols:

• Objective Impression Scoring: An impression estimator (neural network trained on impression-annotated data) is used to automatically score the output speech along each impression dimension. This enables assessment of monotonicity and coverage as the control vector is swept (see Figs. 3 and 4 in the paper).
• Speaker Identity Preservation: Cosine similarity in SSL embedding space (e.g., Resemblyzer) is measured between the output and original speaker; typically, identity is well preserved unless impression modulation is extreme.
• Crowdsourced Listening Tests: Participants rate changes along targeted impression axes (e.g., “Is this sample more feminine than the reference?”), and evaluate overall naturalness via MOS. Strong, monotonic perceptual changes corroborate control effectiveness. LLM-driven impression controls are also evaluated by preference testing; LLM-modulated samples receive dominant preference for matching specific descriptions.

Step	Module/Action
Reference	Speech $\rightarrow$ Speaker encoder $\rightarrow$ Disentangled embedding $\mathbf{x}$ (adversarial GRL)
User Input	Natural language description $\rightarrow$ Impression vector $\mathbf{v}$ (via LLM or manual)
Synthesis	Combine $\mathbf{x}$ and $\mathbf{v}$ $\rightarrow$ Synthesis decoder $\rightarrow$ Waveform
Evaluation	Objective scoring (impression estimator), identity similarity, subjective rating

5. Technical and Mathematical Elements

The backend involves several key mathematical models:

• Impression Vector: $\mathbf{v} \in \mathbb{R}^{11}$
• Adversarial Disentanglement: Employs a gradient reversal layer (GRL) to remove impression information from $\mathbf{x}$; dropout regularizes the embedding.
• Control Module: Projects and fuses $\mathbf{x}$ and $\mathbf{v}$, enabling independent manipulation:

$$\text{Speaker embedding}: \quad \mathbf{x}_{\text{projected}} = \text{FC}_1(\mathbf{x})$$

$$\text{Impression embedding}: \quad \mathbf{v}_{\text{projected}} = \text{FC}_2(\mathbf{v})$$

$$\text{Condition vector}: \quad \mathbf{c} = [\mathbf{x}_{\text{projected}}, \mathbf{v}_{\text{projected}}]$$

• Adversarial loss and MSE: Ensure that $\mathbf{x}$ does not leak impression dimensions, and that $\mathbf{v}$ is faithfully mapped.
• LLM Prompting: Maps user descriptions to Likert scale values for each impression dimension.

6. Applications and Implications

This impression control methodology supports a wide range of real-world and research applications:

• Customizable TTS and Voice Assistants: Synthesize speech in any perceptual style (e.g., “urgent, friendly, dark, bright”) on demand.
• Audiobooks and Accessibility: Adjust narrator impressions as per content or listener preference.
• Interactive Voice Platforms: Support for real-time modulation of impression by users in virtual environments or games.
• Research in Voice Perception: Enables systematic paper of how changes in high-level voice characteristics drive listener perception, independent of speaker identity.
• Downstream TTS Research: Forms the basis for building impression-controlled datasets and for evaluating style generalization in zero-shot settings.

7. Significance and Future Directions

Voice impression control, especially in zero-shot scenarios and with LLM interfaces, represents a major step toward fully interpretable, expressive, and universally accessible speech synthesis. Objective and subjective results demonstrate that:

• The method achieves fine-grained, monotonic, and highly interpretable control over diverse impression dimensions.
• Speaker identity is preserved under realistic modulation.
• LLM-driven specification democratizes use, allowing non-experts to flexibly request voice qualities.

A plausible implication is that such frameworks will enable next-generation multi-modal interfaces, research in social and emotional communication, and principled exploration of voice manipulation for privacy or personalization. Challenges for future work include extending the number and subtlety of impression axes, integrating environmental or contextual controls, and ensuring naturalness under extreme modulations.