Classification of the syllables sound using wavelet, Renyi entropy and AR-PSD features

Kristomo, Domy; Hidayat, Risanuri; Soesanti, Indah

doi:10.1109/cspa.2017.8064931

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Most studies on speech recognition systems use utterance data in the form of isolated words (Masood et al, 2015;Hidayat et al, 2018;Raczynski, 2018;Sawant and Deshpande, 2018;Tomchuk, 2018;Winursito et al, 2018). Others developed syllable-based speech recognition systems (Can and Artuner, 2013;Soe and Theins, 2015;Kristomo et al, 2017). Isolated word recognition systems are preferable because they have high accuracy and require less-complicated algorithms; however, most developed systems add other methods to the system.…”

Section: Introductionmentioning

confidence: 99%

Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics

Hidayat¹,

Winursito²

2020

IJTech

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Studies are constantly developing and improving speech recognition systems, especially their accuracy. This study developed an isolated word recognition system by using the syllable number characteristics of speech signals that will be recognized. First, the syllable number of speech signals to be recognized was detected, and then, the detection results were used to call one of the database groups that matched the syllable number characteristics. This method was designed to reduce the error possibility through a matching process between test data features and database features. This study used Mel frequency cepstral coefficients (MFCC) for feature extraction and the K-nearest neighbor (KNN) method for classification. Three versions of the proposed method were designed. The results showed that version three increased the accuracy by 4% compared to the conventional recognition system. Version three had the fastest computational time compared to the other methods. The addition of syllable detection algorithms in version three increased the computational time by only 0.151 s compared to the conventional MFCC method. The data cut length and threshold value for the filter also influenced the speech recognition system accuracy.

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Section: Introductionmentioning

confidence: 99%

Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics

Hidayat¹,

Winursito²

2020

IJTech

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“…Several previous studies has been done using MLP classifier [2,3,8,9] for speech classification. In [5], three discrete wavelet families (db, sym, and coif) with a different number of coefficients was used and evaluated with two classifiers (GMM and MLP).…”

Section: Introductionmentioning

confidence: 99%

“…Feature Set 2 is the RE features. Feature set 3 is the combination of AR-PSD and statistical features in frequency and time domain (PSDS) [2]. Feature Set 4 is the combination of AR-PSD and the RE features after selected by using Correlation-based feature selection method or CFS (RPSDS).…”

Section: Introductionmentioning

confidence: 99%

“…The experiment result showed that Daubechies is the most effective mother wavelet compared to Haar and Coif. In [2], The MLP was used for classifying syllables sound by using DWT, Renyi Entropy (RE), and Autoregressive Power Spectral Density (AR-PSD) features. This paper presents the classification of Indonesian CV syllables sound signal by using the MLP in varying number of hidden neuron, and the signal processing by using DWT, RE, AR-PSD, and statistical for generating features [10].…”

Section: Introductionmentioning

confidence: 99%

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Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron

2018

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Abstract. The research on signal processing of syllables sound signal is still the challenging tasks, due to non-stationary, speaker-dependent, variable context, and dynamic nature factor of the signal. In the process of classification using multi-layer perceptron (MLP), the process of selecting a suitable parameter of hidden neuron and hidden layer is crucial for the optimal result of classification. This paper presents a speech signal classification method by using MLP with various numbers of hidden-layer and hidden-neuron for classifying the Indonesian Consonant-Vowel (CV) syllables signal. Five feature sets were generated by using Discrete Wavelet Transform (DWT), Renyi Entropy, Autoregressive Power Spectral Density (AR-PSD) and Statistical methods. Each syllable was segmented at a certain length to form a CV unit. The results show that the average recognition of WRPSDS with 1, 2, and 3 hidden layers were 74.17%, 69.17%, and 63.03%, respectively.

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RenyiBS: Renyi entropy basis selection from wavelet packet decomposition tree for phonocardiogram classification

Safara

Ramaiah

2020

J Supercomput

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Classification of the syllables sound using wavelet, Renyi entropy and AR-PSD features

Cited by 5 publications

References 15 publications

Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics

Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics

Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron

RenyiBS: Renyi entropy basis selection from wavelet packet decomposition tree for phonocardiogram classification

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