Classification of Japanese Consonants in terms of Structured Modeling and Geometrical Interpretation of Dynamic Characteristics in Transient Parts

语音信号的暂态部分是近年来语音识别研究的热点。Furui提出了δ倒谱系数来提取语音信号的动态特征，获得了比以往更高的识别率[1986]。然而，应该注意的是，包括动态特性在内的常规方法依赖于具有语音信号格式特征的线性预测。我们采用自然观察系统的结构模型进行语音建模，并采用几何解释对瞬态部分进行分类。一个自然观测滤波器（NOF）由一系列具有相同截止频率的一阶滤波器组成：第一级低通滤波器，后跟高通滤波器。利用适当的系数，NOF可以用一阶滤波器每级输出的线性组合重构输入信号。我们称之为自然观测系数（noc）。由于每个NOC都是截止频率的函数，因此可以通过多截止频率NOC模式的时间过程来分析信号。本文应用日语辅音、摩擦音和顿音的NOC模式来研究语音信号的瞬态部分。因此，我们获得了可区分的NOC模式，这些模式可能反映了日语辅音、摩擦音和顿音以及后面的元音的瞬时结构。另一方面，我们使用了瞬态部分的几何解释。在p维希尔伯特空间中确定一个二维平面，使系数矢量的轨迹尽可能的直线化。我们使用了三种类型的参数，包括线性预测系数、LPC倒谱系数和noc，它们是结构化参数。因此，与其他两个国家相比，国有石油公司表现出一条直线。限制LPC倒谱系数，平滑渐进的轨迹到下一个元音有助于高识别率。

Transient part of speech signals has been of great interest these years in speech recognition. Furui proposed delta cepstrum coefficients to extract a dynamic characteristic of speech signals and achieved higher recognition rate than ever [1986]. It should be noted, however, that conventional approaches including the dynamic characteristics depend on a linear prediction that features formats of speech signals. We employed a structural model named the natural observation system for speech modeling and a geometrical interpretation to classify the types of transient parts.A natural observation filter (NOF) consists of a series of first order filters with the same cutoff frequency : a low pass filter of a first stage followed by high pass filters. The NOF can reconstruct an input signal with a linear combination of outputs at each stage of first order filters, employing appropriate coefficients. We called them natural observation coefficients (NOCs). We can analyze signals by the time courses of NOCs with multi-cutoff frequencies (NOC pattern), because each NOC is a function of the cutoff frequency. We applied the NOC pattern for Japanese consonants, fricatives and stops, to study the transient parts of speech signals. Consequently, we obtained distinguishable NOC patterns that probably reflect an instantaneous structure of Japanese consonants, fricatives and stops, and following vowels.On the other hand, we used a geometrical interpretation of the transient parts. A two-dimensional plane in the p-dimensional Hilbert space was determined so that the trajectory of coefficients vector becomes as straight as possible. We use three types of parameters, including linear prediction coefficients, LPC cepstrum coefficients, and NOCs which are structured parameters. As a result, NOCs show a rather straight line than the other two. Limiting to LPC cepstrum coefficients, the smoothly progressive trajectory to a following vowel contributed to the high recognition rate.