Stabilizing Instruction Supervision for Instruct-TTS via Controllable Diversification and Drift Filtering
Stabilizing Instruct-TTS via controllable diversification and drift filtering, improving instruction-following to 56.4%.
Key Findings
Methodology
The paper proposes a data-centric stabilization method, enhancing coverage and fidelity through controllable instruction diversification, LLM-based drift filtering, and attribute-aligned supervision. Specifically, LLM generates diverse instruction variants, drift filtering removes semantically drifted instructions, and attribute-aligned supervision enhances speech synthesis controllability.
Key Results
- On the Chinese split of InstructTTSEval, the method improves instruction-following from 34.5% without fine-tuning and 51.0% with naive fine-tuning to 56.4%.
- Constrained rewriting reduces semantic drift from 40.4% to 15.4%.
- Ablation studies confirm the complementarity of the three mechanisms, and the drift taxonomy may generalize to other instruction-driven generation tasks.
Significance
This research addresses instruction supervision instability, enhancing instruction-following capability in Instruct-TTS systems, especially in low-level speech synthesis control. It holds significant academic value and provides a more reliable framework for industrial applications.
Technical Contribution
Building on existing methods, this paper introduces a data-centric stabilization strategy that significantly improves instruction generation coverage and fidelity. The proposed drift filtering mechanism offers a novel solution to semantic drift and enhances speech synthesis controllability through attribute-aligned supervision.
Novelty
This is the first application of data-centric stabilization strategies in Instruct-TTS systems, addressing semantic drift in instruction generation through a combination of controllable diversification and drift filtering.
Limitations
- The method may still experience drift when handling extremely complex instructions.
- Requires substantial labeled data for effective drift filtering.
Future Work
Future research directions include optimizing drift filtering algorithms to reduce dependency on labeled data and exploring the applicability of this method to other generative tasks.
AI Executive Summary
In the field of speech synthesis, Instruct-TTS systems use natural language instructions to control voice style, emotion, and prosody. However, existing methods often experience semantic drift during instruction generation, leading to unstable supervision signals and affecting system generalization. This paper proposes a data-centric stabilization method that enhances instruction generation coverage and fidelity through controllable instruction diversification, LLM-based drift filtering, and attribute-aligned supervision.
In experiments, the proposed method significantly improves instruction-following accuracy on the Chinese split of InstructTTSEval, from 34.5% without fine-tuning and 51.0% with naive fine-tuning to 56.4%. Constrained rewriting reduces semantic drift from 40.4% to 15.4%. Ablation studies confirm the complementarity of the three mechanisms, and the drift taxonomy may generalize to other instruction-driven generation tasks.
This research holds significant academic value and provides a more reliable framework for industrial applications. Future research directions include optimizing drift filtering algorithms to reduce dependency on labeled data and exploring the applicability of this method to other generative tasks.
Deep Analysis
Background
Speech synthesis technology is shifting from fixed tags and numeric controls to Instruct-TTS systems, where users specify style, emotion, and prosody through natural language. However, existing methods often experience semantic drift during instruction generation, leading to unstable supervision signals and affecting system generalization.
Core Problem
Instruct-TTS systems often experience semantic drift during instruction generation, leading to unstable supervision signals and affecting system generalization. Semantic drift refers to the phenomenon where generated instructions deviate from the original control intent.
Innovation
The paper proposes a data-centric stabilization method that enhances instruction generation coverage and fidelity through controllable instruction diversification, LLM-based drift filtering, and attribute-aligned supervision. Controllable diversification generates diverse instruction variants through constrained rewriting, drift filtering removes semantically drifted instructions, and attribute-aligned supervision enhances speech synthesis controllability.
Methodology
- �� Data Preparation: Generate instructions using structured label data and audio perturbations.
- �� Controllable Diversification: Generate diverse instruction variants through constrained rewriting.
- �� Drift Filtering: Use LLM to filter out semantically drifted instructions.
- �� Attribute-Aligned Supervision: Enhance speech synthesis controllability through attribute alignment.
Experiments
Experiments are conducted on the Chinese split of InstructTTSEval, using the CosyVoice 2.0-0.5B model for fine-tuning. The experimental design includes baseline comparisons, drift filtering, and attribute-aligned supervision ablation studies.
Results
Experimental results show significant improvements in instruction-following accuracy, speech synthesis naturalness, and controllability. Constrained rewriting reduces semantic drift from 40.4% to 15.4%.
Applications
The method can be applied to scenarios requiring high-precision speech synthesis control, such as audiobooks, virtual assistants, and content creation.
Limitations & Outlook
The method may still experience drift when handling extremely complex instructions. Requires substantial labeled data for effective drift filtering. Future research directions include optimizing drift filtering algorithms to reduce dependency on labeled data.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. You have a recipe, but each time you cook, the recipe description changes slightly, sometimes even misleading you. To ensure you always make a delicious dish, you decide to create a stable recipe. First, you make sure each step of the recipe is clear and doesn't deviate from the original intent. Then, you adjust the recipe according to different taste preferences but keep the core steps unchanged. Finally, you constantly check and adjust the recipe to ensure the dish always meets your expectations. This is like the method in this paper, stabilizing instruction generation through controllable diversification and drift filtering to ensure the quality and consistency of speech synthesis.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to give instructions to your character, but sometimes the game misunderstands your instructions, causing the character to do strange things. To avoid this, you decide to make a plan. First, you ensure each instruction is very clear and won't be misunderstood. Then, you adjust the instructions according to different game scenarios but keep the core strategy unchanged. Finally, you constantly check and adjust the instructions to ensure the character's actions always meet your expectations. This is like the method in this paper, stabilizing instruction generation through controllable diversification and drift filtering to ensure the quality and consistency of speech synthesis.
Glossary
Instruct-TTS
A technology that uses natural language instructions to control speech synthesis.
Used in the paper for generating speech.
Semantic Drift
The phenomenon where instructions deviate from the original semantics during generation.
A problem addressed in the paper.
LLM
A machine learning model used for generating and understanding natural language.
Used for generating and filtering instructions.
Attribute-Aligned Supervision
A method to enhance speech synthesis controllability through attribute alignment.
Used to improve speech synthesis control precision.
Drift Filtering
A method to improve instruction generation quality by filtering out semantically drifted instructions.
Used to enhance instruction generation fidelity.
Open Questions Unanswered questions from this research
- 1 How to improve drift filtering efficiency without increasing labeled data?
- 2 How to reduce semantic drift when handling extremely complex instructions?
Applications
Immediate Applications
Audiobooks
Improve audiobook speech synthesis quality through stable instruction generation.
Virtual Assistants
Enhance virtual assistant speech control capabilities, improving user experience.
Long-term Vision
Content Creation
Support more complex content creation scenarios through high-precision speech synthesis.
Abstract
Instruct-TTS systems expand structured style labels into natural-language training instructions through LLM rewriting, yet we find that over 40% of unconstrained rewrites contain semantic drift that corrupts supervision and weakens generalization. We formalize this problem as instruction supervision instability and propose a data-centric stabilization recipe that jointly improves coverage and fidelity through three mechanisms: controllable instruction diversification for systematic expansion, LLM-based drift filtering for quality control, and attribute-aligned supervision that grounds prosody control in acoustic perturbations. On the Chinese split of InstructTTSEval, our recipe raises instruction-following from 34.5% without fine-tuning and 51.0% with naive fine-tuning to 56.4%, while constrained rewriting reduces drift from 40.4% to 15.4%. Ablations confirm the three mechanisms are complementary, and the drift taxonomy may generalize to instruction-driven generation beyond TTS.