Ferreira Aníbal J S
Department of Electrical and Computer Engineering, University of Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal.
J Acoust Soc Am. 2007 Oct;122(4):2389-404. doi: 10.1121/1.2772228.
This paper addresses the problem of automatic identification of vowels uttered in isolation by female and child speakers. In this case, the magnitude spectrum of voiced vowels is sparsely sampled since only frequencies at integer multiples of F0 are significant. This impacts negatively on the performance of vowel identification techniques that either ignore pitch or rely on global shape models. A new pitch-dependent approach to vowel identification is proposed that emerges from the concept of timbre and that defines perceptual spectral clusters (PSC) of harmonic partials. A representative set of static PSC-related features are estimated and their performance is evaluated in automatic classification tests using the Mahalanobis distance. Linear prediction features and Mel-frequency cepstral coefficients (MFCC) coefficients are used as a reference and a database of five (Portuguese) natural vowel sounds uttered by 44 speakers (including 27 child speakers) is used for training and testing the Gaussian models. Results indicate that perceptual spectral cluster (PSC) features perform better than plain linear prediction features, but perform slightly worse than MFCC features. However, PSC features have the potential to take full advantage of the pitch structure of voiced vowels, namely in the analysis of concurrent voices, or by using pitch as a normalization parameter.
本文探讨了女性和儿童单独发出元音时的自动识别问题。在这种情况下，浊音元音的幅度谱采样稀疏，因为只有F0整数倍的频率才有意义。这对忽略音高或依赖全局形状模型的元音识别技术的性能产生了负面影响。提出了一种新的基于音高的元音识别方法，该方法源于音色概念，并定义了谐波分量的感知频谱簇（PSC）。估计了一组具有代表性的与静态PSC相关的特征，并使用马氏距离在自动分类测试中评估了它们的性能。线性预测特征和梅尔频率倒谱系数（MFCC）系数用作参考，使用44名说话者（包括27名儿童说话者）发出的五个（葡萄牙语）自然元音声音的数据库来训练和测试高斯模型。结果表明，感知频谱簇（PSC）特征的性能优于普通线性预测特征，但略逊于MFCC特征。然而，PSC特征有潜力充分利用浊音元音的音高结构，即在分析同时发声时，或将音高用作归一化参数。
