Aims and Fit of Module

This module aims to let students understand spoken language processing and develop an understanding of the acoustic of speech signals and corresponding phonetics; time and frequency-based digital analysis of the speech signals; machine learning-based robust, scalable, and adaptive speech processing; automatic speech recognition and speech synthesis. This module also aims to let students get familiar with recent popular research outcomes of speech recognition and speech synthesis, and prepare students to develop spoken language processing systems of practical use.

Learning outcomes

A. Demonstrate a
systematic and
critical
understanding of
the challenges of
Data Mining and Big
Data Analytics.
B. Demonstrate a
theoretical and
practical
understanding of
sound, and time
domain processing.
Discuss the
limitations of the
techniques
employed. Function
effectively as an
individual, and as
a member or leader
of a team. Evaluate
the effectiveness
of own and team
performance.
C. Demonstrate a
theoretical and
practical
understanding of
frequency domain
processing of
speech. Discuss the
limitations of the
techniques
employed. Function
effectively as an
individual, and as
a member or leader
of a team. Evaluate
the effectiveness
of own and team
performance.
D. Demonstrate an
understanding of
the speech
processing
pipeline, and the
mathematical models
describing these
processes. Discuss
the limitations of
the techniques
employed. Function
effectively as an
individual, and as
a member or leader
of a team. Evaluate
the effectiveness
of own and team
performance.
E. Appreciate and
critically evaluate
the new research
and applications in
speech processing.
These new
techniques should
evidence some
originality and
meet a combination
of societal, user,
business, and
customer needs as
appropriate.

Method of teaching and learning

Students will be expected to attend two hours of formal lectures as well as to participate in two hours of supervised practical (tutorial/lab) classes in a typical week. Students will be asked to devote eight and half hours of unsupervised time for reflection and consideration of lecture material and will be required to research and read widely on the subject, and where possible use their personal experiences from work placements.