Farah Nazlia Che Kassim
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Farah Nazlia Che Kassim
Official Name
Farah Nazlia, Che Kassim
Alternative Name
Kassim, Farah Nazlia Che
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Scopus Author ID
57208820556

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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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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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DT-CWPT based Tsallis Entropy for Vocal Fold Pathology Detection

2020-10-26 , Farah Nazlia Che Kassim , Vikneswaran Vijean , Muthusamy H. , Zulkapli Abdullah , 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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Implementation of intelligent signal processing method for vocal fold pathology detection through speech signals

2021 , Farah Nazlia Che Kassim

Human produce voice through lungs and vocal folds or vocal cords in the larynx. The presence of pathologies or diseases in vocal cords in the larynx. The presence of pathologies or diseases in vocal folds could have an impact on the normal vibratory patterns and sound quality. The vocal fold pathology detection was a difficult task because some current diagnosis such as laryngoscopy and video endoscopy are invasive and it needs expert and exact knowledge to analyze based on voice signal processing i.e. an automated vocal fold pathology detection system to find various parameters or features obtained from the voice signal is proposed. This method is non-invasive and can be useful as detection tool to assist doctors to make better decisions on patients' condition for further clinical examinations. Mot researchers used features in frequency-domain to find the voice parameters. A few limitations were discovered such as the loss of time-domain information while performing the frequency transformation making it difficult to define and diagnose specific voice disorders clinically. Also, majority of works are limited to pairwise classification problems and less focus on the classification of the exact pathology. This study investigates the performance of features derived from the Dual-Tree Complec Wavelet Packet Transform (DT-CWPT) with energy and entropies measures quantified with two classifiers, k-Nearest Neighbours (k-NN) and Support Vector Machine (SVM). The DT-CWPT introduces the complex coefficients in time and frequency scale which delivers a better time-frequency resolution, provides approximately shift-invariance and good directional selectivity where these properties are lacking in traditional wavelet transform. Decomposition is done on the voice signals energy and entropies to signify the complexity of voice signals in the time and frequency domains. Five sets of datasets obtained from the Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD) are used. In order to reduce the high dimensionality feature of multiclass datasets, feature selections using ReliefF algorithm and Genetic algorithm (GA) are proposed to reduce redundancy features and obtain the optimum features for classification. This automatic voice pathologies detection experimented with file-based and frame-based analyses for pairwise (normal and abnormal class) and multiclass classification of the abnormal voice pathologies (Vocal Fold Cysts, Vocal Nodules, Polyp and Paralysis). It is observed that the best pairwise analysis achieved 99.73% to 100% accuracy meanwhile, 93.90% to 99.64% of average accuracy achieved in multiclass. The presented study 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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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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