Abstract
Dysarthric speech exhibits abnormal prosody and significant speaker variability, presenting persistent challenges for automatic speech recognition (ASR). While text-to-speech (TTS)-based data augmentation has shown potential, existing methods often fail to accurately model the pathological rhythm and acoustic style of dysarthric speech. To address this, we propose DARS, a dysarthria-aware rhythm-style synthesis framework based on the Matcha-TTS architecture. DARS incorporates a multi-stage rhythm predictor optimized by contrastive preferences between normal and dysarthric speech, along with a dysarthric-style conditional flow matching mechanism, jointly enhancing temporal rhythm reconstruction and pathological acoustic style simulation. Experiments on the TORGO dataset demonstrate that DARS achieves a Mean Cepstral Distortion (MCD) of 4.29, closely approximating real dysarthric speech. Adapting a Whisper-based ASR system with synthetic dysarthric speech from DARS achieves a 54.22% relative reduction in word error rate (WER) compared to state-of-the-art methods, demonstrating the framework's effectiveness in enhancing recognition performance.
Abstract (translated)
构音障碍语音表现出异常的语调和显著的说话人变异，这对自动语音识别（ASR）构成了持续挑战。虽然基于文本到语音（TTS）的数据增强方法显示出潜力，但现有方法往往无法准确模拟构音障碍语音的病理节奏和声学风格。为了解决这一问题，我们提出了DARS框架——一个基于Matcha-TTS架构、针对构音障碍语音感知的节律-风格合成框架。DARS整合了多阶段的节奏预测器，该预测器通过正常和构音障碍话语之间的对比偏好进行优化，并且包含了一个模拟构音障碍声学风格的条件流匹配机制，共同增强了时间节奏重建和病理声学风格模拟的能力。 在TORGO数据集上的实验表明，DARS实现了4.29的平均倒谱失真（MCD），与实际构音障碍语音非常接近。当使用DARS生成的合成构音障碍话语来适应基于Whisper的ASR系统时，该系统的词错误率（WER）相比现有最佳方法降低了54.22%，这证明了该框架在提高识别性能方面的有效性。
URL
https://arxiv.org/abs/2603.01369
