Mujunen R, Leinonen L, Kangas J, Torkkola K
Department of Phonetics, University of Helsinki, Finland.
Folia Phoniatr (Basel). 1993;45(3):135-44.
The [s] samples of 11 women, psychoacoustically classified as acceptable/unacceptable, were studied with the self-organizing map, the neural network algorithm of Kohonen. The measurement map had been previously computed with nondisordered speech samples. Fifteen-component spectral vectors, analyzed with the map, were calculated from short-time FFT spectra at 10-ms intervals. The degree of audible acceptability correlated with the location of the sample on the map. Spectral model vectors in different map locations depicted distinguishing spectral features in the [s] samples analyzed. The results demonstrate that self-organized maps are suitable for the extraction and measurement of acoustic features underlying psychoacoustic classifications, and for on-line visual imaging of speech.
对11名女性的[s]样本进行了研究，这些样本经心理声学分类为可接受/不可接受，采用了自组织映射，即科霍宁的神经网络算法。测量映射先前已用无紊乱语音样本计算得出。通过该映射分析的15维谱向量，是根据以10毫秒间隔的短时快速傅里叶变换谱计算得出的。可听可接受程度与样本在映射上的位置相关。不同映射位置的谱模型向量描绘了所分析的[s]样本中的显著谱特征。结果表明，自组织映射适用于提取和测量心理声学分类背后的声学特征，以及用于语音的在线视觉成像。
