通过模糊期望最大化在音频预测扩散模型中增强失联性语音转换
Wen-Shin Hsu1,2, Guang-Tao Lin1, Wei-Hsun Wang3,4,5,6
1Department of Medical Information, Chung Shan Medical University, Taichung 402201, Taiwan.
Diagnostics (Basel, Switzerland)
|December 17, 2024
概括
这项研究引入了一种使用模糊期望最大化 (FEM) 和扩散模型的新语音转换方法,以改善失节性语音的语音预测. 这种方法提高了发言障碍患者的言语可理解性和自然性.
科学领域:
- 语音处理 语音处理
- 人工智能的人工智能
- 辅助技术 辅助技术 辅助技术
背景情况:
- 发音障碍,一种运动语音障碍,由于神经损伤而损害了语音理解能力.
- 现有的语音转换 (VC) 系统在与失节性语音的变化作斗争,特别是在语音预测方面.
- 准确的语音预测对于改善异关节性VC质量和沟通至关重要.
研究的目的:
- 开发一种新的方法,用于增强语音预测在失节性语音.
- 为改善发声障碍患者语音转换的质量和可理解性.
- 将模糊预期最大化 (FEM) 与扩散模型集成为强大的语音处理.
主要方法:
- 结合模糊预期最大化 (FEM) 集群与扩散概率模型 (DPM).
- 利用扩散模型进行噪声模拟,以增强语音信号的强度.
- 使用FEM进行代的语音边界优化,以减少不确定性.
- 在萨尔兰大学语音障碍数据集上训练了系统,在Mel谱域中处理语音.
主要成果:
- 显著提高了语音预测准确度和整体语音转换质量.
- 与StarGAN-VC和CycleGAN-VC相比,在自然性,可理解性和扬声器相似性方面获得了更高的平均意见分 (MOS).
- 对于轻度和重度的脱节症来说,表现出较低的文字错误率 (WER),表明语音可理解性得到了增强.
结论:
- 整合FEM和扩散模型大大改善了处理失关节性言语不规则的情况.
- 这种方法表现出强度,在没有扬声器编码器的情况下保持语音自然性和可理解性.
- 这种方法为开发可靠的辅助通信技术和针对失节症的个性化语音治疗提供了有希望的基础.
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