Identifikasi Pola Suara Pada Bahasa Jawa Meggunakan Mel Frequency Cepstral Coefficients (MFCC)

Almanfaluti, Istian Kriya; Sugiono, Judi Prajetno

doi:10.30865/mib.v4i1.1793

Cited by 1 publication

(2 citation statements)

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“…Using hierarchical concepts, deep learning becomes an approach to problem-solving in computer learning systems that can learn a complex concept by combining more straightforward concepts. The process of feature extraction from human voices has been performed using MFCC in a study conducted by (9). In their work, spoken words are converted to digital signals by converting sound waves into a set of numbers, adapted to specific codes to identify the words.…”

Section: Related Studiesmentioning

confidence: 99%

“…One of the most popular machine learning techniques for pattern recognition and classification is the convolution neural network (CNN) (1)(2)(3)(4)(5)(6). One of the most commonly used feature extraction techniques for voice recognition is the mel-frequency cepstral coefficients (MFCC) (7)(8)(9)(10). Extracted features are fed into the CNN to produce the voice recognition model.…”

Section: Introductionmentioning

confidence: 99%

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Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

et al. 2021

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Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.

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Section: Related Studiesmentioning

confidence: 99%