在耳鼻喉科声学分析的快速发展的情景
Marco Fantini1,2, Gabriele Ciravegna3, Alkis Koudounas3
1Ear, Nose, and Throat Unit, Koelliker Hospital, Turin, ITA.
Cureus
|December 13, 2024
概括
先进的声学语音分析,利用头部测量和人工智能 (AI),提供比传统方法更可靠的声音健康评估. 这种进化有望改善语音障碍的诊断和早期检测.
科学领域:
- 语音和听力科学 语言和听力科学
- 生物医学工程 生物医学工程
- 计算语言学 计算语言学
背景情况:
- 语音分析已经从主观感知方法发展到复杂的数字信号处理.
- 传统的声学参数,如基本频率,动和闪,在强度和可变性方面都有局限性.
- 现代方法整合了多参数结构,以进行全面的声质评估.
研究的目的:
- 审查语音分析技术的演变.
- 为了突出转向更可靠的声学和声谱测量方法的转变.
- 探索人工智能对语音诊断和查的影响.
主要方法:
- 审查传统的声学语音分析参数 (基本频率,动,闪,噪声与声调比率).
- 讨论先进的指标,包括切普斯特尔指标.
- 探索多参数方法,结合持续元音和连续语音.
- 检查新兴的人工智能 (AI) 和深度学习应用程序.
主要成果:
- 与传统的语音分析参数相比,Cepstral测量显示出更好的准确性和可靠性.
- 多参数构造提供了更全面的声质评估.
- 人工智能,特别是深度学习,显示出提高诊断精度和实现非侵入性查的巨大潜力.
结论:
- 由于其可靠性和实用性,先进的声学和脑管测量在临床实践中越来越受欢迎.
- 人工智能驱动的语音分析代表了范式的转变,有望改善语音病理的早期检测和治疗.
- 语音分析的未来在于整合复杂的计算工具,以进行精确和高效的健康评估.
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