Indonesian Vowel Recognition Using Artificial Neural Network Based On the Wavelet Features

Amalia, Nadya; Fahrudi, Arfan E.; Nasrulloh, Amar Vijai

doi:10.11591/ijece.v3i2.2325

Cited by 3 publications

(3 citation statements)

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“…The result of this study showed that the DWT is a method that is more efficient and effective in extracting the Indonesian phonemes compared with the WPT as shown by the effectiveness ratio of 60% versus 40% and efficiency ratio of 57% versus 43% [11]. In the context of speech classification, there are several previous studies that combining feature extraction methods based on Wavelet [13]- [17], MFCC [16]- [18], LPC [16]- [17], LPCC [16], and the classifier methods such as MLP [13,18,19], HMM [20], GMM [19], and LDA [14,17].…”

Section: Introductionmentioning

confidence: 89%

“…The term 1/√s is used for energy normalization in the varying scale. In the wavelet research, the selection of the most suitable mother wavelet is still a relative question mark among researchers [13]. Figure 1 shows the structure of the WT.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Figure 1 shows the structure of the WT. In feature extraction using WT, the process of choosing the right mother wavelet is crucial for optimal result of classification [13,22]. The mother wavelets filter used in this study are Haar, Daubechies, and Coiflet.…”

Section: Feature Extractionmentioning

confidence: 99%

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Wavelet Based Feature Extraction for The Indonesian CV Syllables Sound

2018

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This paper proposes the comb ined methods of Wavelet Transform (WT) and Euclidean Distance (ED) to estimate the expected value of the possib ly feature vector of Indonesian syllab les. This research aims to find the b est properties in effectiveness and efficiency on performing feature extraction of each syllab le sound to b e applied in the speech recognition systems. This proposed approach which is the state-of-the-art of the previous study consist of three main phase. In the first phase, the speech signal is segmented and normalized. In the second phase, the signal is transformed into frequency domain b y using the WT. In the third phase, to estimate the expected feature vector, the ED algorithm is used. Th e result shows the list of features of each syllab les can b e used for the next research, and some recommendations on the most effective and efficient WT to b e used in performing syllab le sound recognition.

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