Speech systems classification based on frequency of binary word features

S. Maithani, M. Din
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引用次数: 7

Abstract

This paper presents a robust method to classify the speech communication systems on the basis of speech coding technique used in digitizing the speech. The method works for both clear and noisy speech conditions, when the level of noise may be unknown or known in terms of bit alterations level up to 30% and can identify coding in as short as 640 bit speech. The noise level is estimated using a technique based on higher order statistics (HOS) using digitized speech output and is independent of the coding used. The classification algorithms used are of two types: Minimum Distance Classifier (MDC) based on Linear Discriminant function (LDF) and Artificial Neural Net (ANN). The features used are based on frequency of binary words, characterizing the speech coding technique used in the speech systems. Three types of codings namely Pulse Code Modulation (PCM), Continuously Variable Slope Delta Modulation (CVSD) and Linear Predictive Coding (LPC) are considered. The classification score obtained for both known and unknown noise level arc compared. It is found that results are far better when level of noise is known in both types of classifiers.
基于二值词特征频率的语音系统分类
基于数字化语音的语音编码技术,提出了一种鲁棒的语音通信系统分类方法。该方法适用于清晰和嘈杂的语音条件,当噪声水平可能未知或已知的比特变化水平高达30%时,可以识别短至640位的语音编码。使用基于高阶统计量(HOS)的技术估计噪声电平,使用数字化语音输出,并且与所使用的编码无关。使用的分类算法有两种:基于线性判别函数(LDF)的最小距离分类器(MDC)和人工神经网络(ANN)。所使用的特征是基于二进制词的频率,表征语音系统中使用的语音编码技术。考虑了三种类型的编码,即脉冲编码调制(PCM),连续变斜率增量调制(CVSD)和线性预测编码(LPC)。对已知和未知噪声等级的分类得分进行了比较。研究发现,当两种分类器都知道噪声水平时,结果要好得多。
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