Speech Difficulties and Clarification: A Systematic Review

Natural language processing helps the computer to process human languages. In general, human language is presented either in the form of text or speech utterances. Speech based computer interaction system helps the computer to understand human language and perform various tasks for verbal instructions and to make intelligent assistive system. Background noise is the most common factor that causes degradation of the quality and intelligibility of speech. The term background noise refers to any unwanted signal that is added to the desired speech signal. In fact, language description is the main motivation behind widely used machine learning (ML) techniques such as Hidden Markov Models, Discriminant Learning, Structured Sequential Learning, Bayesian Learning, and Adaptive Learning. In addition, speech clarification is being used by machine learning as a large-scale, realistic application to assess a particular method's performance and to address new challenges arising due to the naturally sequential and dynamic character of speech. At the other side, although speech clarification occurs for some applications, it remains largely an unexplained issue for most of the applications. This overview article provides readers an insight about modern ML techniques as they are currently used and relevant to future speech explanation systems. Articles are organized according to the most important ML paradigms that are already popular and can contribute significantly to voice recognition.