Speech recognition is an interdisciplinary subfield of natural language processing (NLP) that facilitates the recognition and translation of spoken language into text by machine. Speech recognition plays an important role in digital transformation. It is widely used in different areas such as education, industry, and healthcare and has recently been used in many Internet of Things and Machine Learning applications. The process of speech recognition is one of the most difficult processes in computer science. Despite numerous searches in this domain, (...) an optimal method for speech recognition has not yet been found. This is due to the fact that there are many attributes that characterize natural languages and every language has its particular highlights. The aim of this research is to provide a comprehensive understanding of the various techniques within the domain of Speech Recognition through a systematic literature review of the existing work. We will introduce the most significant and relevant techniques that may provide some directions in the future research. (shrink)

Authorship Analysis (AA) is a natural language processing field that examines the previous works of writers to identify the author of a text based on its features. Studies in authorship analysis include authorship identification, authorship profiling, and authorship verification. Due to its relevance, to many applications in this field attention has been paid. It is widely used in the attribution of historical literature. Other applications include legal linguistics, criminal law, forensic investigations, and computer forensics. This paper aims to provide an (...) overview of the work done and the techniques applied in the authorship analysis domain. The examination of recent developments in this field is the principal focus. Many different criteria can be used to define a writer’s style. This paper investigates stylometric features in different author-related tasks, including lexical, syntactic, semantic, structural, and content-specific ones. A lot of classification methods have been applied to authorship analysis tasks. We examine many research studies that use different machine learning and deep learning techniques. As a means of pointing the direction for future studies, we present the most relevant methods recently proposed. The reviewed studies include documents of different types and different languages. In summary, due to the fact that each natural language has its own set of features, there is no standard technique generically applicable for solving the AA problem. (shrink)