用合成语音提升空管语音识别准确率
Synthetic Audio Generation Framework for Air Traffic Control Speech Recognition
- 构建音色模拟管道，生成带口音的空管语音数据
- 混合真实与合成数据使错误率降低37%
- 适合需要高精度语音识别的航空领域研究者
尽管自动语音识别系统在通用领域表现优异，但在空管（ATC）场景中仍面临严重挑战，主要由于通道噪声强、非母语（L2）英语口音普遍及数据稀缺。本文提出一种专门针对该问题的合成语音生成框架，通过结合文本转语音、语音转换、L2到L1口音转换以及一种新型可控的L1到L2口音转换技术，模拟真实空管语音的声学特性。在ATCO2数据集上使用Whisper模型进行实验，结果表明：仅使用合成数据微调或混合真实与合成数据微调，均显著优于未微调基线和仅用真实数据微调的基线，词错误率降低37%。
原文摘要 · Abstract (English)
Automatic Speech Recognition (ASR) systems, despite achieving remarkable accuracy in general-purpose domains with native speech (L1), struggle in domains like Air Traffic Control (ATC) due to strong channel noise, a presence of non-native (L2) English accents, and data scarcity. We propose a synthetic data generation pipeline with acoustical properties simulations specifically designed to address this lack of real data to improve recognition accuracy in the ATC domain. Our approach leverages a combination of neural generation techniques, including Text-to-Speech, Voice Conversion, L2-to-L1 accent conversion, and a novel controllable L1-to-L2 accent conversion framework built to simulate accented speech. Our experiments with the Whisper model on the ATCO2 corpus demonstrate that fine-tuning with either synthetic data alone, or a mix of real and synthetic data, significantly improves the word error rate over out-of-the-box and real data only baselines respectively.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。