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Word Recognition using A Two-Dimensional Mel-cepstrum under Noisy Environments.

嘈杂环境下使用二维梅尔倒谱的单词识别。

基本信息

批准号：
63550253
负责人：
KITAMURA Tadashi
金额：
$ 1.34万
依托单位：
Nagoya Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1988
资助国家：
日本
起止时间：
1988 至 1989
项目状态：
已结题

项目摘要

The purpose of this research is to offer a new method for word recognition under noisy environments. In this study white noise generated by computer simulation and colored noise recorded in the Nagoya station are used. A speaker- independent word recognition method of ten Japanese digits using a two- dimensional mel-cepstrum(TDMC) is proposed. TDMC is defined as the two- dimensional Fourier transform of mel-frequency scaled logarithm spectra in the frequency and time domains and consists of average features and dynamic features of the two-dimensional mel-log spectra, Experimental results in this study are shown as follows.1. Speech analysis-synthesis system using a TMDC and its estimation; The structure of speech analysis-synthesis system using a TMDC is proposed in order to study the size of the TDMC for synthesizing good quality speech. It is shown that the frequency of the required area of the TDMC is less than about 10Hz.2. Reference patterns robust for the variation of signal-to-noise ratio (SNR) of input speech; In this study a single set of TDMCs of noise-added reference patterns with desired SNR is used for word recognition under noisy environments. Experimental results show that a recognition method using this reference pattern set is more effective than a usual method.3. Distance measures for a word recognition method robust for the variation of SNR of input speech; Distance measures using a combination of dynamic and average features of the TDMC is proposed. It is shown that dynamic features are more important than average features for word recognition under noisy environment.

本研究的目的是为噪声环境下的单词识别提供一种新的方法。本研究采用计算机模拟产生的白噪声和名古屋站实测的彩色噪声。提出了一种基于二维梅尔倒谱（TDMC）的独立于说话人的10位日文数字词识别方法。TDMC定义为mel-frequency标度对数谱在频域和时域上的二维傅里叶变换，由二维mel-log谱的平均特征和动态特征组成，本研究的实验结果如下。基于TMDC的语音分析合成系统及其估计为了研究合成高质量语音所需的TDMC的大小，提出了基于TDMC的语音分析合成系统的结构。结果表明，TDMC所需区域的频率小于10hz左右。参考模式对输入语音信噪比变化具有鲁棒性；在本研究中，使用一组具有期望信噪比的加噪参考模式的tdmc进行噪声环境下的单词识别。实验结果表明，采用该参考模式集的识别方法比常规方法更有效。一种对输入语音信噪比变化具有鲁棒性的词识别方法的距离度量提出了一种结合TDMC的动态特征和平均特征的距离度量方法。结果表明，在噪声环境下，动态特征比平均特征更为重要。