语音信号的MFCC参数:一种替代形式式即时声道长度估计的替代方法
P Vasquez-Serrano1, J Reyes-Moreno1, Rodrigo Capobianco Guido2
1Escuela de Ing. Eléctrica, Electrónica y de Telecomunicaciones (E3T), Universidad Industrial de Santander, Bucaramanga, Colombia.
概括
梅尔频率塞普斯特拉系数 (MFCCs) 为估计声道长度 (VTL) 提供了一种新的方法,在语音依赖模型中显示出前景. 虽然可以与跨扬声器模型中的传统形式分析进行比较,但MFCCs为动态VTL评估提供了强大的和计算效率高的方法.
科学领域:
- 语音处理 语音处理
- 声学语音学的声音学.
- 生物医学工程 生物医学工程
背景情况:
- 传统上,形式频率被用来估计声道长度 (VTL).
- 梅尔频率切普斯特拉系数 (MFCC) 提供了简洁的光谱包裹表示,但它们与VTL的关系较少被探索.
- VTL通常被视为静态扬声器特征,但动态估计正在引起人们的兴趣.
研究的目的:
- 调查使用MFCC用于VTL估计的优点和缺点,与传统的基于形式的方法相比.
- 使用现代实时磁共振成像 (rtMRI) 探索动态VTL估计.
- 在扬声器依赖和跨扬声器场景中评估基于MFCC的VTL估计性能.
主要方法:
- 使用统计建模来分析MFCC和VTL估计的形式.
- 实时磁共振成像 (rtMRI) 用于捕捉动态声道运动.
- 使用USC-TIMIT磁共振视频数据集,与音频信号一起进行2D实时关分析.
主要成果:
- 基于MFCC的VTL估计在扬声器依赖模型中显示出更高的性能.
- 在跨扬声器建模中,MFCC显示性能与基于formant的方法相美.
- 基于MFCC的VTL估计被发现具有可接受的计算复杂性.
结论:
- 多形声道测量器为动态声道长度估计提供了可行和高效的替代方案.
- 该研究强调了MFCC的潜力,特别是在音箱依赖的场景中.
- 这些发现支持MFCC作为语音分析和VTL评估的强大特征,补充传统方法.
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