Fantini Marco, Ciravegna Gabriele, Koudounas Alkis, Cerquitelli Tania, Baralis Elena, Succo Giovanni, Crosetti Erika
Ear, Nose, and Throat Unit, Koelliker Hospital, Turin, ITA.
Ear, Nose, and Throat Unit, San Feliciano Hospital, Rome, Italy.
Cureus. 2024 Nov 11;16(11):e73491. doi: 10.7759/cureus.73491. eCollection 2024 Nov.
The field of voice analysis has experienced significant transformations, evolving from basic perceptual assessments to the incorporation of advanced digital signal processing and computational tools. This progression has facilitated a deeper understanding of the complex dynamics of vocal function, particularly through the use of acoustic voice analysis within a multidimensional evaluation framework. Traditionally, voice analysis relied on parameters such as fundamental frequency, jitter, shimmer, and noise-to-harmonic ratio, which, despite their utility, have faced criticism for variability and lack of robustness. Recent developments have led to a shift toward more reliable metrics such as cepstral measures, which offer improved accuracy in voice quality assessments. Furthermore, the integration of multiparametric constructs underscores a comprehensive approach to evaluating vocal quality, blending sustained vowels, and continuous speech analyses. Current trends in clinical practice increasingly favor these advanced measures over traditional parameters due to their greater reliability and clinical utility. Additionally, the emergence of artificial intelligence (AI), particularly deep learning, holds promise for revolutionizing voice analysis by enhancing diagnostic precision and enabling efficient, non-invasive screening methods. This shift toward AI-driven approaches signifies a potential paradigm change in voice health, suggesting a future where AI not only aids in diagnosis but also the early detection and treatment of voice-related pathologies.
语音分析领域经历了重大变革，从基本的感知评估发展到纳入先进的数字信号处理和计算工具。这一进展促进了对发声功能复杂动态的更深入理解，特别是通过在多维评估框架内使用声学语音分析。传统上，语音分析依赖于诸如基频、抖动、闪烁和噪声与谐波比等参数，尽管这些参数有用，但因变异性和缺乏稳健性而受到批评。最近的发展导致向更可靠的指标转变，如倒谱测量，其在语音质量评估中提供了更高的准确性。此外，多参数结构的整合强调了一种评估语音质量的综合方法，融合了持续元音和连续语音分析。由于其更高的可靠性和临床实用性，临床实践中的当前趋势越来越倾向于这些先进措施而非传统参数。此外，人工智能（AI）的出现，尤其是深度学习，有望通过提高诊断精度和实现高效、无创的筛查方法来彻底改变语音分析。这种向人工智能驱动方法的转变标志着语音健康领域潜在的范式变化，预示着一个未来，即人工智能不仅有助于诊断，还能用于语音相关疾病的早期检测和治疗。
