Based on research conducted by the Institute of Qur'anic Sciences (IIQ) as many 65% Muslims in Indonesia are illiterate Qur'an. In previous studies, was detection Arabic word pronunciation errors against non-natives using Mel Frequency Cepstral Coefficient (MFCC) and Support Vector Machine (SVM) methods with a test result 54.6%. Due to low accuracy results this study aims design build system th...

Speaker recognition is one of the most essential tasks in the signal processing which identifies a person from characteristics of voices . In this paper we accomplish speaker recognition using Mel-frequency Cepstral Coefficient (MFCC) with Weighted Vector Quantization algorithm. By using MFCC, the feature extraction process is carried out. It is one of the nonlinear cepstral coefficient functio...

This paper provides an efficient approach for text-independent speaker identification using the Inverted Mel-frequency Cepstral Coefficients as feature set and Finite Doubly Truncated Gaussian Mixture as Model (FDTGMM). Over the years, Mel-Frequency Cepstral Coefficients (MFCC), modeled on the human auditory system, has been used as a standard acoustic feature set for speech related application...

In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lowertime lags, while the higher-lag autocorrelation coefficients are least affected, this method discards the lower-lag autocorrelation coefficients and uses o...

Economical speaker recognition solution from degraded human voice signal is still a challenge. This article covering results of an experiment which targets to improve feature extraction method for effective identification audio with the help data science. Every speaker’s has identical characteristics. Human ears can easily identify these different characteristics and classify audio. Mel-Frequen...

A K-Nearest Neighbour Algorithm involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform Speech signal feature extraction for the task of speaker accent recognition. Mel-Frequency Cepstral Coefficient is effectively used to perform the feature extraction of the input signal. For each input signal the mean of the MFCC matrix is used for pattern recognition .The K-nearest neig...

The paper present effective method for recognition of digit, numbers. Most of speech recognition systems contain two main modules as follow “feature extraction” and “feature matching”. In this project, (MFCC) Mel Frequency Cepstrum coefficient algorithm is used to simulate feature extraction module. Using this algorithm, the Cepstral Coefficients are calculated on Mel frequency scale. VQ (vecto...

Recognizing human emotions through vocal channel has gained increased attention recently. In this paper, we study how used features, and classifiers impact recognition accuracy of emotions present in speech. Four emotional states are considered for classification of emotions from speech in this work. For this aim, features are extracted from audio characteristics of emotional speech using Linea...