通过结合低级别描述符和基础模型表示,对病态声音进行强有力的评估
IEEE journal of biomedical and health informatics
|December 15, 2025
概括
本研究介绍了VOQANet和VOQANet+的深度学习模型,用于客观地评估语音质量. VOQANet+通过将语音基础模型嵌入与声学特征相结合来增强病态语音评估,提高准确性和稳定性.
科学领域:
- 语音处理 语音处理
- 生物医学工程 生物医学工程
- 人工智能的人工智能是人工智能.
背景情况:
- 感知语音质量评估对于诊断语音障碍至关重要.
- 像CAPE-V和GRBAS这样的传统方法是主观的,容易产生评级者之间的变化.
- 需要客观的语音分析方法来克服传统尺度的局限性.
研究的目的:
- 引入VOQANet,这是一个使用注意力机制和SFM嵌入式语音质量评估的深度学习模型.
- 提出VOQANet+,将SFM嵌入式与低级声学特征 (,闪,HNR) 整合起来,以提高性能.
- 使用母音级和句子级语音数据评估模型的概括性.
主要方法:
- 开发了使用注意力机制和语音基础模型 (SFM) 嵌入的VOQANet.
- 通过将自主监督的SFM嵌入式与声学描述符相结合,创建了VOQANet+:动,闪和和声波与噪声比 (HNR).
- 评估了基于母音 (PVQD-A) 和基于句子 (PVQD-S) 的语言模型,用于病态语音质量评估 (PVQD).
主要成果:
- 基于句子的输入显示出更高的准确性,特别是在患者层面.
- 对于CAPE-V和GRBAS维度的RMSE和Pearson相关性,VOQANet的表现优于基线模型.
- VOQANet+实现了卓越的性能增长,并在噪音条件下保持了稳健性.
结论:
- 将SFM嵌入式与低级声学特征相结合,可以提高病理语音评估的准确性和稳定性.
- VOQANet和VOQANet+为语音障碍诊断和监测提供客观可靠的解决方案.
- 由于其性能和稳定性,拟议的模型对现实世界的应用,包括远程医疗非常有希望.
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