NOPE-HYPE: A Structured Simulation Workflow for Robust Speech-to-Text Across Diverse Acoustic Environments

2026-09-09

AI Summary

为解决语音转文字系统在不同声学环境下的鲁棒性问题，提出NOPE-HYPE工作流，结合可控环境模拟器与参数搜索优化模型训练。

Abstract

Robust speech-to-text translation systems should perform reliably across diverse acoustic conditions, yet practical pipelines lack controllable tools for systematic environment exploration. Large speech models remain sensitive to unseen acoustic conditions, as training data rarely cover the full range of real environments.We present NOPEHYPE, a structured training workflow that combines a controllable environment simulator, coverage-optimal environment reduction on Power Spectral Density (PSD) templates, and a small, interpretable hyperparameter search over simulator knobs. We show that simulator-generated noise achieves performance comparable to balanced realnoise training across Whisper and SeamlessM4T models, provide principled environment prototype sets, and identify practical default simulator configurations from a structured 27-run hyperparameter sweep.

Problem

• robust speech-to-text
• acoustic environments
• training data
• environment exploration

Innovation

• controllable environment simulator
• Power Spectral Density (PSD) templates
• hyperparameter search