Thesiss

This dissertation demonstrates a framework for incremental model selection and processing of highly variant speech transcripts and user-generated text. The system reduces natural language processing (NLP) ambiguity by segmenting text by domain, allowing for domain-specific downs…

A fundamental challenge for current research on speech science and technology is understanding and modeling individual variation in spoken language. Individuals have their own speaking styles, depending on many factors, such as their dialect and accent as well as their socioecon…

The Arabic language has many different dialects, they must be identified before Automatic Speech Recognition can take place. This thesis examines the difficult task of properly identifying various Arabic dialects. We present a novel design of an Arabic dialect identification sys…