A study on the influence of speaking style change into prosodic individuality information.

说话人识别主要是利用从语音的谱包络中提取的声学特征。该特征与声道有关，不受说话人的有意影响。因此，该特征被广泛用于说话人识别，但是这容易受到传输特性的影响。近年来，由于韵律特征在噪声环境中很难受到影响，人们开始使用基音周期等韵律特征。在本研究中，我们进行了三项研究，研究基频与说话风格的关系以及提高说话人识别能力的研究，具体如下.基频与说话风格的关系。我们记录了基频，响度和语速不同的声音，由许多人说话。我们比较了说话风格的基频分布。结果表明，这些频率分布随说话者和说话风格的不同而不同， ...更多信息 用平均基频归一化后的分布趋于变小.基于DP迹的文本相关说话人确认研究了DP迹信息对说话人确认的影响。结果表明，仅利用DP迹信息进行说话人确认是可行的，但与传统的特征参数比较方法相比，说话人确认率较低.说话人识别中的特征参数变换。提出了一种特征参数变换方法来提高说话人确认的准确率。转换分两个阶段进行。在第一阶段，我们通过减去平均值，然后除以标准差来标准化参数。在第二阶段中，我们通过每个特征向量的范数对参数进行归一化。使用孤立发出的元音的实验结果表明，应用变换后，平均说话人验证率提高了约3个点。少

Speaker recognition have mainly used acoustic feature extracted from spectral envelope of speech sounds. The feature, which is related to vocal tract, is not intentionally affected by speakers. Therefore, this feature is widely used on speaker recognition, but this is easily to be affected by a transmission characteristic. In recently, there are used prosodic features such as pitch, because there are hard to be affected in environment with many noises. In this research, we performed three studies, which studies relation to fundamental frequency and the speaking style and a study for improvement of speaker recognition, as follows.1. The relation to fundamental frequency with speaking style.We recorded voices that fundamental frequency, loudness and speech rate are different, were spoken by many speakers. We compared distributions of fundamental frequency with speaking styles. As results, these frequency distributions were different with speakers and speaking styles, but a difference of … More these distributions which were normalized by its average fundamental frequency tended to become small.2. Text-dependent speaker verification by using DP trace as speaking style.For focusing on speaking styles, we examined an effect of using the DP trace information on the dynamic time warping. As results, it was shown that speaker verification was possible using just DP trace information, but speaker verification rate was lower than conventional technique to compare feature parameter.3. Feature parameter translation for speaker recognition.We propose a feature parameter transformation method to improve the accuracy of speaker verification. The transformation is performed in two stages. In the first stage, we standardize a parameter by subtracting the average, and then dividing it by the standard deviation. In the second stage, we normalize the parameter by the norm of each feature vector. The results of the experiments using vowels uttered in isolation showed an approximately 3-point improvement in the average speaker verification rate after applying the transformation. Less