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

从结构化建模和瞬态部分动态特性的几何解释角度对日语辅音进行分类

基本信息

批准号：
02805044
负责人：
HORI Jun-ichi
金额：
$ 1.22万
依托单位：
Niigata University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1990
资助国家：
日本
起止时间：
1990 至 1991
项目状态：
已结题

项目摘要

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.

近年来，语音信号的瞬态部分在语音识别领域引起了极大的兴趣。 Furui提出了δ倒谱系数来提取语音信号的动态特征，并取得了比以往更高的识别率[1986]。然而，应当注意，包括动态特性的传统方法依赖于以语音信号的格式为特征的线性预测。我们采用称为自然观察系统的结构模型进行语音建模，并使用几何解释来对瞬态部分的类型进行分类。自然观察滤波器（NOF）由一系列具有相同截止频率的一阶滤波器组成：低通滤波器第一级后接高通滤波器。 NOF 可以使用适当的系数，通过一阶滤波器每一级输出的线性组合来重建输入信号。我们称它们为自然观测系数（NOC）。我们可以通过具有多个截止频率（NOC 模式）的 NOC 的时间进程来分析信号，因为每个 NOC 都是截止频率的函数。我们将 NOC 模式应用于日语辅音、摩擦音和塞音，以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、摩擦音和塞音以及后续元音的瞬时结构。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，使得系数向量的轨迹变得尽可能直。我们使用三种类型的参数，包括线性预测系数、LPC 倒谱系数和结构化参数 NOC。因此，NOC 比其他两个显示出一条相当直线的直线。受限于 LPC 倒谱系数，后续元音的平滑渐进轨迹有助于提高识别率。