Speech Synthesis Recognition

AY2017/2018 Semester 1

The course begins with an overview of natural language processing (NLP) to situate speech synthesis and recognition within industry and reviews familiar applications of these systems. Necessary topics concerning phonetics and phonology are introduced/reviewed, with a particular focus on acoustic phonetics. The topic of speech synthesis is then covered, with a focus on concatenative synthesis and statistical parametric systems, but formant-based and articulatory synthesis will also be covered in detail. In relation to these latter topics, students will learn about modeling the structures of the vocal tract to produce articulatory-based systems and the types of applications these have in speech research. Next the topic of speech recognition is covered. Students will learn about the design parameters of these systems and the tie-ins with other topics in NLP (such as language models and syntactic and semantic parsers). The Hidden Markov Model will be discussed in detail and its application to the task of recognizing speech in conjunction with acoustic analysis and modeling of the speech signal.