This paper presents a research proposal for identifying acoustic features that preserve ASR fidelity during speech enhancement. It proposes a framework to analyze the impact of acoustic feature modifications on ASR performance, develop a feature-aware enhancement architecture, and establish evaluation protocols prioritizing recognition accuracy over perceptual metrics.
Key findings
Neural speech enhancement systems improve perceptual quality but not necessarily ASR performance.
A systematic framework is proposed to analyze the impact of different acoustic features on ASR performance.
A multi-objective training paradigm is introduced to optimize for signal fidelity and ASR compatibility.
The proposed method includes detailed experimental designs covering multiple benchmarks and risk analysis.
Limitations & open questions
The research is still in the proposal stage and requires actual implementation and testing.
The effectiveness of the proposed method is yet to be validated against real-world ASR systems.