Enhancing Moral Awareness for Racial Unity Through Islamic and Asian Civilization Course (TITAS): An Empirical Research from Non-Muslim Students’ Perspective

This article attempted to investigate the significance of the Islamic and Asian Civilization Course (TITAS) according to the point of view of non-Muslim students to form a harmonious view towards Islam besides forming and cultivating racial unity in Malaysia. This study was also conducted with expectations that TITAS will be the impetus to create moral awareness amongst non-Muslim students towards Islam and Muslims other than giving ideas that multiracial is assimilable through TITAS. This matter was aligned with the objective of the Islamic Civilization study for non-Muslim students and the goals of TITAS introduced in 1983. This study was conducted using a quantitative approach involving 203 non-Muslim students from Universiti Pendidikan Sultan Idris as the study sample. The mean was 3.81 which depicts a high-level achievement of the goals and objectives of TITAS. This showed that TITAS was able and managed to predispose moral awareness amongst non-Muslim students towards Islam and Muslims. It is also proposed that TITAS continues to be maintained as the mainstream syllabus beyond the pre-university level

Automatic Speaker Characterization; Automatic Identification of Gender, Age, Language and Accent from Speech Signals (Automatische sprekercharacterisatie; Automatische identificatie van geslacht, leeftijd, taal en accent uit stemopnamen)

Speech signals carry important information about a speaker such as age, gender, language, accent and emotional/psychological state. Automatic recognition of speaker characteristics has a wide range of commercial, medical and forensic applications such as interactive voice response systems, service customization, natural human-machine interaction, recognizing the type of pathology of speakers, and directing the forensic investigation process. This research aims to develop accurate methods and tools to identify different physical characteristics of the speakers. Due to the lack of required databases, among all characteristics of speakers, our experiments cover gender recognition, age estimation, language recognition and accent/dialect identification. However, similar approaches and techniques can be applied to identify other characteristics such as emotional/psychological state.For speaker characterization, we first convert variable-duration speech signals into fixed-dimensional vectors suitable for classification/regression algorithms. This is performed by fitting a probability density function to acoustic features extracted from the speech signals. Since the distribution of acoustic features is complex, Gaussian mixture models (GMM) are applied to model the distribution of acoustic features. Due to lack of data, it is not possible to build a separate acoustic model for short utterances. Therefore, parametric utterance adaptation methods have been applied to adapt the universal background model (UBM) to the characteristics of utterances. The parameters of each adapted GMM characterize the corresponding utterance. An effective approach involves adapting UBM to speech signals using the Maximum-A-Posteriori (MAP) scheme. Then, the Gaussian means of the adapted GMM are extracted and concatenated to form a Gaussian mean supervector for the given utterance.Finally, a classification or regression algorithm is used to identify the speakercharacteristics. While effective, Gaussian mean supervectors are of a highdimensionality resulting in high computational cost and difficulty in obtaining a robust model in the context of limited data. In the field of speaker recognition, recent advances using the i-vector framework have increased the classification accuracy considerably. This framework, which provides a compact representation of an utterance in the form of a low-dimensional feature vector, applies a simple factor analysis on GMM means. Motivated by this success, the i-vector framework is applied to the age estimation problem. In this approach, each utterance is modeled by its corresponding i-vector. Then, a within-class covariance normalization (WCCN) technique is used for session variability compensation. Finally, a least squares support vector regression (LSSVR) is applied to estimate the age of speakers. The proposed method is trained and tested on telephone conversations of the National Institute for Standard and Technology (NIST) 2010 and 2008 speaker recognition evaluation databases. Evaluation results show that the proposed method yields significantly lower mean absolute estimation error and a higher Pearson correlation coefficient between chronological speaker age and the estimated speaker age comapred to different conventional schemes. Finally, the effect of some major factors influencing the proposed age estimation system, namely utterance length and spoken language are analyzed.Our experiments on age estimation show that GMM weights carry importantinformation about the speaker. However, the state-of-the-art language/speakerrecognition systems usually do not use this information. In this research, anon-negative factor analysis (NFA) approach is developed for GMM weightdecomposition and adaptation. This modeling suggests a new low-dimensionalutterance representation method, which uses a factor analysis similar tothat of the i-vector framework. The obtained subspace vectors are thenapplied in conjunction with i-vectors to the language/dialect recognitionproblem. The suggested approach is evaluated on the NIST 2011 and RATSlanguage recognition evaluation (LRE) corpora and on the QCRI Arabic dialect recognition evaluation (DRE) corpus. The assessment results show that the proposed adaptation method yields more accurate recognition results compared to three conventional weight adaptation approaches, namely maximum likelihood re-estimation, non-negative matrix factorization, and a subspace multinomial model. Experimental results also show that the intermediate level fusion of i-vectors and NFA subspace vectors improves the performance of the state-of-the-art i-vector framework. Motivated by the success of the NFA framework in Language/dialect recognition we introduce a hybrid architecture of the NFA approach and the i-vector frameworks for the speaker age estimation problem. Evaluation on the NIST 2010 and 2008 SRE corpora shows that the proposed hybrid architecture improves the results of the i-vector framework considerably.Bahari M.H., ''Automatic speaker characterization. Identification of Gender, Age, Language and Accent from Speech Signals'', Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen, KU Leuven, May 2014, Leuven, Belgium.status: publishe

Intelligent Drilling Rate Predictor

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Speaker age estimation using hidden markov model weight supervectors

This paper proposes a new approach for speaker age estimation. In this method, speakers are modeled by their corresponding Hidden Markov Model (HMM) weight supervectors. Then, Weighted Supervised Non-Negative Matrix Factorization (WSNMF) is applied to reduce the dimension of the input space. Finally, a Least Squares Support Vector Regressor (LS-SVR) is employed to estimate the age of speakers using the obtained low-dimensional vectors. Evaluation results on a corpus of read and spontaneous speech in Dutch confirms the effectiveness of the proposed scheme. © 2012 IEEE.Bahari M.H., Van hamme H., ''Speaker age estimation using hidden markov model weight supervectors'', 11th international conference on information science, signal processing and their applications - ISSPA 2012, July 3-5, 2012, 517-521 pp., Montreal, Quebec, Canada.status: publishe

Speaker age estimation and gender detection based on supervised non-negative matrix factorization

In many criminal cases, evidence might be in the form of telephone conversations or tape recordings. Therefore, law enforcement agencies have been concerned about accurate methods to profile different characteristics of a speaker from recorded voice patterns, which facilitate the identification of a criminal. This paper proposes a new approach for speaker gender detection and age estimation, based on a hybrid architecture of Weighted Supervised Non-Negative Matrix Factorization (WSNMF) and General Regression Neural Network (GRNN). Evaluation results on a corpus of read and spontaneous speech in Dutch confirms the effectiveness of the proposed scheme. © 2011 IEEE.Bahari M.H., Van hamme H., ''Speaker age estimation and gender detection based on supervised non-negative matrix factorization'', Proceedings IEEE workshop on biometric measurements and systems for security and medical applications, pp. 27-32, September 28, 2011, Milan, Italy.status: publishe

Speaker adaptation using maximum likelihood general regression

In this paper, a new method called Maximum Likelihood General Regression (MLGR) is introduced for speaker adaptation. Gaussian means of a speaker independent (SI) model are adapted to the data of a new speaker by assuming a non-linear mapping from the SI Gaussian means to the adapted Gaussian means. MLGR performs a non-linear regression between ML estimates of the means and the SI means using General Regression Neural Network. The proposed method is evaluated on the Wall Street Journal database. Evaluation results show that the suggested scheme outperforms different conventional approaches in the case of short adaptation utterances. We also mathematically prove that the Gaussian means of the adapted model using the MLGR converges to their ML estimates in the case of long adaptation utterances. © 2012 IEEE.Bahari M.H., Van hamme H., ''Speaker adaptation using maximum likelihood general regression'', 11th international conference on information science, signal processing and their applications - ISSPA 2012, July 3-5, 2012, 29-34 pp., Montreal, Quebec, Canada.status: publishe