强大的基本频率检测算法不受人类声音声的存在的影响
Itsuki Kitayama1, Kiyohito Hosokawa1,2, Shinobu Iwaki3
1Department of Otorhinolaryngology and Head & Neck Surgery, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan.
The Journal of the Acoustical Society of America
|December 24, 2024
概括
准确估计基本频率 (fo) 对于语音分析至关重要. 一个新的算法,SFEEDS,显著提高了对估计的准确性,特别是对于的声音,超越了传统的方法.
科学领域:
- 声学 声学 在声学方面
- 语音科学 语言科学
- 生物医学工程 生物医学工程
背景情况:
- 基本频率 (fo) 对于评估声特征至关重要.
- 现有的估计方法难以准确,特别是对于的声音.
- 需要一个强大而准确的算法,用于对失声语的语音估计.
研究的目的:
- 引入一种新的算法,即通过主导和序列 (SFEEDS) 强调的光谱基于fo估计器,以改进fo估计.
- 将SFEEDS的性能与传统的估计方法进行比较.
- 为了评估SFEEDS在正常和声声音样本中的准确性.
主要方法:
- 开发并应用了SFEEDS算法,这是对频谱方法的增强.
- 分析了454个语音样本,包括正常语音和声语音.
- 从光谱图确定了地面真相,并将其与SFEEDS和传统方法的估计进行了比较.
主要成果:
- 与传统方法相比,SFEEDS在所有语音样本中显示出明显更高的声值估计准确度.
- 在常规方法表现较低的声样本中,SFEEDS 保持了准确性.
- SFEEDS算法证明了对声音粗的稳定性,并显著减少了亚和声错误.
结论:
- SFEEDS算法为正常和的声音提供了准确的基本频率估计.
- 与传统方法相比,SFEEDS提供了显著的进步,特别是在具有挑战性的语音条件下.
- 这种算法有可能改善临床语音评估和研究.
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