Rokiah Abdullah
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Preferred name
Rokiah Abdullah
Official Name
Rokiah, Abdullah
Alternative Name
Abdullah, Rokiah
Abdullah, R.
Main Affiliation
Scopus Author ID
57208820919
Researcher ID
EJA-4882-2022

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DT-CWPT based Tsallis Entropy for Vocal Fold Pathology Detection

2020-10-26 , Farah Nazlia Che Kassim , Vikneswaran Vijean , Muthusamy H. , Abdullah Z. , Rokiah Abdullah , Palaniappan R.

The study of voice pathology has become one of the valuable methods of vocal fold pathology detection, as the procedure is non-invasive, affordable and can minimise the time needed for the diagnosis. This paper investigates the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) based Tsallis entropy for vocal fold pathology detection. The proposed method is tested with healthy and pathological voice samples from Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD). A pairwise classification using k-Nearest Neighbors (k-NN) classifier gave 91.59% and 85.09% accuracy for MEEI and SVD database respectively. Higher classification accuracy of 93.32% for MEEI and 85.16% for SVD database achieved using Support Vector Machine (SVM) classifier.

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Real and complex wavelet transform using singular value decomposition for malaysian speaker and accent recognition

2021-01-01 , Rokiah Abdullah , Vikneswaran Vijean , Muthusamy H. , Farah Nazlia Che Kassim , Abdullah Z.

This paper presents a new approach for Malaysian speaker and accent recognition using wavelet feature extraction method, namely Wavelet Packet Transform (WPT), Discrete Wavelet Packet Transform (DWPT) and Dual Tree Complex Wavelet Packet Transform (DT-CWPT). Since Singular Value Decomposition (SVD) was based on factorization and summarization technique which reduces a rectangular matric, it is applied on those features to evaluate the performance for speaker and accent recognition. The features are derived from wavelets and SVD classified with three different classifiers namely k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). In this work, English digits (0–9) and Malay words database uttered from 75 undergraduate students of Universiti Malaysia Perlis (UniMAP) which are Malays, Chinese and Indian. The Malay words had a combination of consonants and vowels in monosyllable and bi-syllable structure. The accuracy of file-based analysis achieved were above 81% while for frame-based analysis, 93.87% and above were obtained using three different classifiers (k-NN, SVM and ELM) for speaker and accent recognition. Through the experiments, it is observed that accent recognition achieved high recognition rate of 100% for both framed-based analysis and file-based analysis using SVM. The experimental results show the proposed features using SVD achieved high accuracy of 100% using SVM through English digits and Malay words in accent recognition. This indicated that feature extraction using wavelets (WPT, DWPT and DT-CWPT) with SVD can achieve a good performance for both English digits and Malay words.

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Optimization of dual-tree complex wavelet packet based entropy features for voice pathologies detection

2020-07-01 , Farah Nazlia Che Kassim , Vikneswaran Vijean , Abdullah Z. , Muthusamy H. , Rokiah Abdullah

The Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) has been successfully implemented in numerous field because it introduces limited redundancy, provides approximately shift-invariance and geometrically oriented signal in multiple dimensions where these properties are lacking in traditional wavelet transform. This paper investigates the performance of features extracted using DT-CWPT algorithms which are quantified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classifiers for detecting voice pathologies. Decomposition is done on the voice signals using Shannon and Approximate entropy (ApEn) to signify the complexity of voice signals in time and frequency domain. Feature selection methods using the ReliefF algorithm and Genetic algorithm (GA) are applied to obtain the optimum features for multiclass classification. It is observed that the best accuracies obtained using DT-CWPT with ApEn entropy are 91.15 % for k-NN and 93.90 % for SVM classifiers. The proposed work provides a promising detection rate for multiple voice disorders and is useful for the development of computer-based diagnostic tools for voice pathology screening in health care facilities.

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Dual-tree complex wavelet packet transform for voice pathology analysis

2020-07-01 , Farah Nazlia Che Kassim , Muthusamy H. , Vikneswaran Vijean , Zulkapli Abdullah , Rokiah Abdullah

Voice pathology analysis has been one of the useful tools in the diagnosis of the pathological voice, as the method is non-invasive, inexpensive, and can reduce the time required for the analysis. This paper investigates feature extraction based on the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) using energy and entropy measures tested with two classifiers, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD) were used. Five datasets of voice samples were used from these databases, including normal and abnormal samples, Cysts, Vocal Nodules, Polyp, and Paralysis vocal fold. To the best of the authors’ knowledge, very few studies were done on multiclass classifications using specific pathology database. File-based and frame-based investigation for two-class and multiclass were considered. In the two-class analysis using the DT-CWPT with entropies, the classification accuracy of 100% and 99.94% was achieved for MEEI and SVD database respectively. Meanwhile, the classification accuracy for multiclass analysis comprised of 99.48% for the MEEI database and 99.65% for SVD database. The experimental results using the proposed features provided promising accuracy to detect the presence of diseases in vocal fold.

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Dual tree complex wavelet packet transform based features for Malaysian speaker and accent recognition

2021 , Rokiah Abdullah

An approach using energy and entropy based on Dual Tree Complex Wavelet Packet Transform (DT-CWPT) has been proposed for Malaysian speaker and accent recognition. The frequency transformation method using Fourier transform is not a very useful tool to analyse the signal as the time-information is lost. Time-frequency, using the wavelet approach is a good tool for the analysis of nonstationary signals both in time and frequency scale. In order to test the accuracy of the proposed method, speech signals are decomposed into 5 levels from energy and entropy derived from WPT and DT-CWPT. The performance is compared with conventional Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) features. Three different classifiers, such as k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are used to evaluate the performance of speaker and accent recognition. The new database was developed using English digits (0-9) and Malay words uttered by 75 undergraduate students of Universiti Malaysia Perlis (UniMAP), consisting the three main accents in Malaysia, which are Malay, Chinese and Indian. The experiments were carried out individually and by consolidating all the features. It was found through the experimental observations that the results employing the energy and entropy-based wavelet are promising and comparable. The best recognition rate achieved was 91.05%, which was computed from the energy and entropy-based wavelet (DT-CWPT) for speaker recognition using Malay words. For accent recognition, the best recognition rate at 94.84% was obtained from MFCC features using Malay words. For combined features, the speaker recognition using Malay words achieved 97.67%. While for accent recognition, the highest recognition for combined features obtained was 98.13%. Although the recognition result yielded a good result, it suffers from large features and long computation time. The feature selection, namely Binary Genetic Algorithm (GA) was applied to select the best subset from original features to reduce the number of features and long computation time. The results demonstrated that the number of features decreased by more than 70%. The computation time using k-NN, SVM, ELM classifier was reduced by at least by 69.5%, 53% and 14%, respectively. It was observed that GA reduces the computation time with a significant percentage while maintaining the recognition rate at a comparable figure with only a slight difference of within 3%. The accent recognition results show that there are significant differences between the three accents in Malaysia using the Malay language. It can be concluded that between the three accents in Malaysia, the native speakers (Malay accent) performed better followed by the Indian accent and Chinese accent. This is because as the native speakers, Malay speakers can pronounce the Malay words more precise, compared to Chinese and Indian. Overall, the results from Malay words show better performance compared to English digits for speaker and accent recognition.

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