Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics. INDEX TERMS Speech recognition, deep neural network, systematic review.

Extending the Technology Acceptance Model (TAM) for studying the e-learning acceptance is not a new research topic, and it has been tackled by many scholars. However, the development of a comprehensive TAM that could be able to examine the e-learning acceptance under any circumstances is regarded to be an essential research direction. To identify the most widely used external factors of the TAM concerning the e-learning acceptance, a literature review comprising of 120 significant published studies from the last twelve years was conducted. The review analysis indicated that computer selfefficacy, subjective/social norm, perceived enjoyment, system quality, information quality, content quality, accessibility, and computer playfulness were the most common external factors of TAM. Accordingly, the TAM has been extended by the aforementioned factors to examine the students' acceptance of e-learning in five different universities in the United Arab of Emirates (UAE). A total of 435 students participated in the study. The results indicated that system quality, computer self-efficacy, and computer playfulness have a significant impact on perceived ease of use of e-learning system. Furthermore, information quality, perceived enjoyment, and accessibility were found to have a positive influence on perceived ease of use and perceived usefulness of e-learning system.

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The Arabic language presents researchers and developers of natural language processing (NLP) applications for Arabic text and speech with serious challenges. The purpose of this article is to describe some of these challenges and to present some solutions that would guide current and future practitioners in the field of Arabic natural language processing (ANLP). We begin with general features of the Arabic language in Sections 1, 2, and 3 and then we move to more specific properties of the language in the rest of the article. In Section 1 of this article we highlight the significance of the Arabic language today and describe its general properties. Section 2 presents the feature of Arabic Diglossia showing how the sociolinguistic aspects of the Arabic language differ from other languages. The stability of Arabic Diglossia and its implications for ANLP applications are discussed and ways to deal with this problematic property are proposed. Section 3 deals with the properties of the Arabic script and the explosion of ambiguity that results from the absence of short vowel representations and overt case markers in contemporary Arabic texts. We present in Section 4 specific features of the Arabic language such as the nonconcatenative property of Arabic morphology, Arabic as an agglutinative language, Arabic as a pro-drop language, and the challenge these properties pose to ANLP. We also present solutions that have already been adopted by some pioneering researchers in the field. In Section 5 we point out to the lack of formal and explicit grammars of Modern Standard Arabic which impedes the progress of more advanced ANLP systems. In Section 6 we draw our conclusion.

This study seeks to explore the effect of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The study has made use of Google Meet© as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. The main hypotheses of this study are related to the effect of COVID-19 on the adoption of Google Meet as COVID-19 rise s various types of fear. During the Coronavirus pandemic, fear due to family lockdown situation, fear of education failure and fear of losing social relationships are the most common types of threat that may face students and teachers/educators. These types of fears are connected with two important factors within TAM theory, which are: perceived ease of use (PEOU) and perceived usefulness (PU), and with another external factor of TAM, which is subjective norm (SN). The results revealed that both data analysis techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases.