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

• 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
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-02805044/
• 关键词: transient part; dynamic characteristics; natural observation filter; consonants; reconstruction; fricatives; stops; trajectory; 後続母音; 自然観測法; 連鎖母音; 調音結合区間; 時係列; 射影; 個別差; 後続母音予測; transient part; dynamic characteristics; natural observation filter; consonants; reconstruction; fricatives; stops; trajectory; 後続母音; 自然観測法; 連鎖母音; 調音結合区間; 時係列; 射影; 個別差; 後続母音予測

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.

言语信号的短暂部分在语音识别中引起了极大的兴趣。 Furui提出的Delta cepstrum系数提取语音信号的动态特征，并比以往任何时候都更高的识别率[1986]。但是，应该指出的是，包括动态特征在内的常规方法取决于具有语音信号格式的线性预测。我们采用了一种称为语音建模的自然观察系统和几何解释的结构模型来对瞬态零件的类型进行分类。自然观察滤波器（NOF）由一系列具有相同截止频率的一阶过滤器组成：第一阶段的低通滤波器，然后是高通滤波器。 NOF可以使用适当的系数在一阶过滤器的每个阶段重建输出信号的输入信号。我们称它们为自然观察系数（NOC）。我们可以通过具有多切口频率（NOC模式）的NOC的时间过程来分析信号，因为每个NOC都是截止频率的函数。我们将NOC模式应用于日本的辅音，摩擦剂和停止，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本辅音，摩擦和停止的瞬时结构，并遵循元音。另一方面，我们使用了对瞬态部分的几何解释。确定了p维希尔伯特空间中的二维平面，以使系数矢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 CEPSTRUM系数和结构化参数的NOC。结果，NOC显示出比其他两个相当直线的。限制了LPC Cepstrum系数，平稳进行轨迹到以下元音有助于高识别率。