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首页> 外文期刊>International Journal of Applied Engineering Research >Analysis on Mel Frequency Cepstral Coefficients and Linear Predictive Cepstral Coefficients as Feature Extraction on Automatic Accents Identification
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Analysis on Mel Frequency Cepstral Coefficients and Linear Predictive Cepstral Coefficients as Feature Extraction on Automatic Accents Identification

机译：基于口音自动识别的特征频率提取的梅尔频率倒谱系数和线性预测倒谱系数分析

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摘要

Automatic Accents Identification is very important for discussion especially within scope of speaker recognition. Some contribution of appropriate techniques uses in Music Recognition and Accent Identification may contributes in improving the recognition rate. Techniques in discussing on music genre identification or accents automatic identification and the combination of both processes still in ambiguous for this field. This paper investigates mainly the processes involved in speech processing or identification includes: acoustic/speech signal, pre-processing, feature extraction, pattern classification and accuracy results. Process of automatic accents identification through speech signals starts with general pre-processing techniques, feature extraction; which in this studies too, comparing within two techniques; Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Cepstral Coefficients (LPCC). While for vocal tract with musical characteristic in used for musical genre identification and the usage of pattern classification for three methods which includes; Hidden Markov Model (HMM), Support Vector Machine (SVM) and Probabilistic Principal Component Analysis. Thus, this paper investigates the feature extraction techniques used in identifying accents to be implemented in Quranic Accents identification and proposed MFCC as better techniques for feature extraction and getting higher accuracies for 93.33% while 86.67% if compared to LPCC.
机译：自动口音识别对于讨论非常重要，尤其是在说话者识别范围内。在音乐识别和口音识别中使用适当技术的一些贡献可能有助于提高识别率。在音乐流派识别或口音自动识别以及这两个过程的组合方面，讨论该技术的技术仍不清楚。本文主要研究涉及语音处理或识别的过程，包括：声音/语音信号，预处理，特征提取，模式分类和准确性结果。通过语音信号自动识别口音的过程始于一般的预处理技术，特征提取。在这项研究中，也将两种技术进行了比较;梅尔频率倒谱系数（MFCC）和线性预测倒谱系数（LPCC）。对于具有音乐特征的声道用于音乐体裁识别和模式分类的三种方法包括：隐马尔可夫模型（HMM），支持向量机（SVM）和概率主成分分析。因此，本文研究了用于识别古兰经口音的口音的特征提取技术，并提出了MFCC作为更好的特征提取技术，与LPCC相比，准确率更高，为93.33％，而准确率则为86.67％。

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