Analysis And Voice Recognition In Indonesian Language Using MFCC And SVM Method

Harvianto, Harvianto; Ashianti, Livia; Jupiter, Jupiter; Junaedi, Suhandi

doi:10.21512/comtech.v7i2.2252

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…In that case, the more delicate components of the Mel frequency spectral envelope can be represented. Still, the dimensionality of the feature vector increases, so MFCCs with less than 20 dimensions [20][21][22][23] or MFCCs and logarithmic energy are often used. 24 The MFCCs and its time-varying component Delta MFCCs, and also the time-varying component of Delta MFCCs, Delta delta MFCCs, are sometimes used.…”

Section: Compress the Amplitude Spectrum By Applying Amentioning

confidence: 99%

Fault detection of contactor using acoustic monitoring

Inoue

Stewart

Entezami

2021

Advances in Mechanical Engineering

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Recently, condition monitoring methods using the sound of the machine have attracted attention. Since approaching high voltage equipment increases the risk of electrocuting, non-contact data acquisition is desirable. Most of the research targets of acoustic monitoring are rotating machines and it is not clear whether it is effective for machines that switch between two states, such as contactors and circuit breakers. In this work, several investigations have been carried out on the acoustic condition monitoring of contactor. The Mel-frequency cepstrum coefficients (MFCCs) were obtained from the sound data of the contactors under normal and simulated fault conditions. Support Vector Machine (SVM) was trained with MFCCs and found that it could detect and diagnose contactor faults with high accuracy.

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Section: Compress the Amplitude Spectrum By Applying Amentioning

confidence: 99%

Fault detection of contactor using acoustic monitoring

Inoue

Stewart

Entezami

2021

Advances in Mechanical Engineering

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“…Mel Frequency Cepstral Coefficients (MFCC) is one of the voice feature extraction techniques that are often used as in [9]- [12], MFCC is used to distinguish one sound from other sounds. By utilizing classification techniques, we can classify MFCC features from voice input to a class that we have specified.…”

Section: Related Workmentioning

confidence: 99%

“…However, the many variations of sound can cause the classification process to look for non-linear correlations. To solve non-linear correlations problem many researchers try to use machine learning techniques [9]- [16], and what is more promising is the classification using deep learning techniques. With this in mind, we decided to try the deep learning approach (Convolutional Neural Network) on the speech recognition module in our educational game application.…”

Section: Related Workmentioning

confidence: 99%

Indonesian Alphabet Speech Recognition for Early Literacy using Convolutional Neural Network Approach

Khrisne

Hendrawati²

2020

jeei

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Games are considered capable of being used as a learning medium that can help teachers to teach children how to pronounce the Indonesian alphabet in early literacy, we try to build one aspect of the game in this study. The approach we use is a speech recognition approach that uses the convolutional neural network method. The results of this study indicate that CNN can recognize speech, with input data is in the form of sound. We use the MFCC feature vector sound feature to make a 3-dimensional matrix of input sound into CNN input. We also use the Sequential CNN architecture made from a simple 10 layer neural network, which produces a model with a small size, approximately only about 6 MB, with high accuracy (84%) and an F-Measure of 0.91.

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“…Meanwhile, Harvianto et. al [30] analyse the voice of Indonesia Language using MFCC and SVM achieved high accuracy which is 91.83%.…”

Section: Introductionmentioning

confidence: 99%

Comparison of CNNs and SVM for voice control wheelchair

Sharifuddin

Nordin

Ali

2020

IJ-AI

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In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+ to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time.

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Analysis And Voice Recognition In Indonesian Language Using MFCC And SVM Method

Cited by 6 publications

References 3 publications

Fault detection of contactor using acoustic monitoring

Fault detection of contactor using acoustic monitoring

Indonesian Alphabet Speech Recognition for Early Literacy using Convolutional Neural Network Approach

Comparison of CNNs and SVM for voice control wheelchair

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