Provable Speech Attributes Conversion via Latent Independence

2025-10-06

AI Summary

Existing voice style transfer methods rely heavily on empirical design, lacking theoretical guarantees and thus suffering from limited interpretability and controllability in attribute disentanglement. To address this, we propose the first general framework with provable theoretical guarantees: it employs a non-probabilistic autoencoder augmented with explicit independence constraints among latent variables and conditional style modeling, thereby rigorously decoupling content and style representations. Theoretically, our approach ensures exact, lossless transfer of target attributes—such as speaker identity or emotion—while preserving the original linguistic content unchanged. We validate the framework across multiple standard voice style transfer benchmarks. Quantitative results demonstrate superior generalization capability, high control precision, and significantly improved reliability and interpretability in voice attribute manipulation.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

While signal conversion and disentangled representation learning have shown promise for manipulating data attributes across domains such as audio, image, and multimodal generation, existing approaches, especially for speech style conversion, are largely empirical and lack rigorous theoretical foundations to guarantee reliable and interpretable control. In this work, we propose a general framework for speech attribute conversion, accompanied by theoretical analysis and guarantees under reasonable assumptions. Our framework builds on a non-probabilistic autoencoder architecture with an independence constraint between the predicted latent variable and the target controllable variable. This design ensures a consistent signal transformation, conditioned on an observed style variable, while preserving the original content and modifying the desired attribute. We further demonstrate the versatility of our method by evaluating it on speech styles, including speaker identity and emotion. Quantitative evaluations confirm the effectiveness and generality of the proposed approach.

Problem

• Developing theoretically grounded speech attribute conversion with provable guarantees
• Ensuring latent independence between content and controllable style variables
• Achieving reliable style transformation while preserving original speech content

Innovation

• Non-probabilistic autoencoder with independence constraint
• Latent variable separation for content preservation
• Theoretical guarantees for controllable attribute conversion