STATISTICAL-ANALYSIS OF WORD-INITIAL VOICELESS OBSTRUENTS - PRELIMINARY DATA
STATISTICAL-ANALYSIS OF WORD-INITIAL VOICELESS OBSTRUENTS - PRELIMINARY DATA
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DOI：
10.1121/1.396977
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发表时间：
1988-07-01
影响因子：
2.4
通讯作者：
DOUGALL, RN
中科院分区：
文献类型：
--
作者：
FORREST, K;WEISMER, G;DOUGALL, RN
描述了一种用于对单词开头的清音构词进行分类的统计过程。应用该分析的数据集由以清音阻塞词开头的单音节单词组成。每个词都被十位演讲者重复了六次，这句话的意思是：我可以再说一遍。使用20ms汉明窗口的快速傅立叶变换(FFT)从阻塞开始到第三个元音周期每隔10ms计算一次。每个FFT被视为一个随机概率分布，根据该分布计算前四个矩(均值、方差、偏度和峰度)。矩由线性谱和Bark变换谱计算。对特定性别的说话者的元音上下文和判别分析的输入数据被汇集在一起。使用由线性谱计算的矩，当计入停顿的动态方面时，92%的清音停顿被正确分类。更重要的是，根据男性的数据构建的模型正确地对女性说话者发出的大约94%的无声塞音进行了分类。当分析中包括所有发音位置时，当使用线性或Bark变换音阶的力矩时，对清音摩擦音的分类不超过80%的正确率。然而，当使用树皮变换光谱的矩时，仅对清音浊音的分类正确率为98%。与停靠点一样，分类模式也适用于不同性别。
A statistical procedure for classifying word-initial voiceless obstruents is described. The data set to which the analysis was applied consisted of monosyllabic words starting with a voiceless obstruent. Each word was repeated six times in the carrier phrase "I can say ...... again" by each of ten speakers. Fast Fourier transforms (FFTs), using a 20-ms Hamming window, were calculated every 10 ms from the onset of the obstruent through the third cycle of the following vowel. Each FFT was treated as a random probability distribution from which the first four moments (mean, variance, skewness, and kurtosis) were computed. Moments were calculated from linear and Bark transformed spectra. Data were pooled across vowel contexts for speakers of a given gender and input to a discriminant analysis. Using the moments calculated from the linear spectra, 92% of the voiceless stops were classified correctly when dynamic aspects of the stop were included. Even more important, the model constructed from the males'' data correctly classified about 94% of the voiceless stops produced by the female speakers. Classification of the voiceless fricatives when all places of articulation were included in the analysis did not exceed 80% correct when the moments from either the linear or Bark transformed scales were used. However, classification of only the voiceless sibilants was 98% correct when the moments from the Bark transformed spectra were used. As with the stops, the classification model held across gender.