Speech Synthesis method for Medical-care equipment by Using Sand-glass Type Neural Network

提出了一种利用5层沙漏型非线性神经网络（SNN（NL 5））从LSP参数进行语音特征提取的新方法。为了合成语音，我们利用SNN（NL 5）的有用能力，用于压缩和恢复的信息。我们对5个元音的LSP参数进行了学习实验，以研究SNN的能力。结果表明：1）SNN（NL 5）压缩后的LSP参数分布与F1-F2共振峰平面的分布相似; 2)SNN（NL 5）第二层和第四层神经元的非线性输出函数从分离元音分布的角度有效地工作。3）为了防止SNN（NL 5）的过学习，第二层和第四层存在最佳神经元数目。对于14阶LSP参数，该数目被确定为20。4)当LSP参数的恢复误差变小时，在平面上有一个较好的性质，可以区分元音。5）SNN（NL 5）可以恢复LSP参数，其精度足以从压缩参数合成语音。

We showed a new scheme to characterize speech from LSP parameters by 5 layers sandglass type nonlinear neural network (SNN(NL5)). In order to synthesize speech, we take advantage of useful abilities of SNN(NL5) for compressing and restoring the information. We performed learning experiments on LSP parameters of 5 vowels to investigate the ability of SNN. The followings were verified, 1)the distribution of LSP parameters compressed by SNN(NL5) are similar to the distribution of F1-F2 formants plane. 2)Nonlinear output function of neural elements in second and fourth layers of SNN(NL5) work effectively from view point of separating the distribution of vowels. 3)In order to prevent SNN(NL5) from over learning, there exists the optimum numbers of neural elements in second and fourth layers. For 14 orders of LSP parameters, this number was determined to be 20. 4)There is a preferable property on the plane to separate the vowels distinctively when the restoring error of LSP parameters becomes less. 5)SNN(NL5) can restore the LSP parameters with accuracy enough to synthesize speech from the compressed parameters.