Course Title: Speech: Phonetics, prosody, perception
and synthesis (11-752)
Department: Language Technologies Institute (LTI)
Units: 12
Semester: Spring
Instructor: Maxine
Eskenazi, Alan W Black
Prerequisite: Knowledge of basic statistics,
good computing skills. No prior experience with speech recognition is
necessary. This course is primarily for graduate students in LTI, CS, Robotics,
ECE, Psychology, or Computational Linguistics. Others by prior permission of
instructor.
Course Description:
The goal of the course is to give the student basic knowledge from
several fields that is necessary in order to pursue research in
automatic speech processing. The course will begin with a study of
the acoustic content of the speech signal. The students will use the
spectrographic display to examine the signal and discover its variable
properties. Phones in increasingly larger contexts will be studied
with the goal of understanding coarticulation. Phonological rules will
be studied as a contextual aid in understanding the spectrographic
display.
The spectrogram will then serve as a first introduction to the basic
elements of prosody. Other displays will then be used to study the
three parts of prosody: amplitude, duration, and pitch. Building on
these three elements, the student will then examine how the three
interact in careful and spontaneous speech.
Next, the students will explore perception. Topics covered will be:
The second part of this course will cover all aspects of speech synthesis.
The whole synthesis process will be covered from both a theoretical and
practical viewpoint.
Subsections of the course will cover:
Each section will describe the problems abstractly and cover what are
considered current solutions with their advantages and disadvantages
highlighted. Practical exercises cast within the Festival Speech
Synthesis System framework will be set, to give experience in solving
actual synthesis problems.
Students need only have a basic knoweldge of speech and language
processing. Some degree of programming and statistical modelling
will be beneficial, but not required.
Course notes:
Course notes and exercise workpages will be provided although some
reading of papers may also be required.
Grading:
Students will have practical exercises throughout the course
as well as one larger project (possibly a group project).
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