A key requirement for developing any innovative system in a
computing environment is to integrate a sufficiently friendly
interface with the average end user. Accurate design of such a
user-centered interface, however, means more than just the
ergonomics of the panels and displays. It also requires that
designers precisely define what information to use and how, where,
and when to use it. Recent advances in user-centered design of
computing systems have suggested that multimodal integration can
provide different types and levels of intelligence to the user
interface. The work of this thesis aims at improving speech
recognition-based interfaces by making use of the visual modality
conveyed by the movements of the lips.
Designing a good visual front end is a major part of this framework.
For this purpose, this work derives the optical flow fields for
consecutive frames of people speaking. Independent Component
Analysis (ICA) is then used to derive basis flow fields. The
coefficients of these basis fields comprise the visual features of
interest. It is shown that using ICA on optical flow fields yields
better classification results than the traditional approaches based
on Principal Component Analysis (PCA). In fact, ICA can capture
higher order statistics that are needed to understand the motion of
the mouth. This is due to the fact that lips movement is complex in
its nature, as it involves large image velocities, self occlusion
(due to the appearance and disappearance of the teeth) and a lot of
non-rigidity.
Another issue that is of great interest to audio-visual speech
recognition systems designers is the integration (fusion) of the
audio and visual information into an automatic speech recognizer.
For this purpose, a reliability-driven sensor fusion scheme is
developed. A statistical approach is developed to account for the
dynamic changes in reliability. This is done in two steps. The first
step derives suitable statistical reliability measures for the
individual information streams. These measures are based on the
dispersion of the N-best hypotheses of the individual stream
classifiers. The second step finds an optimal mapping between the
reliability measures and the stream weights that maximizes the
conditional likelihood. For this purpose, genetic algorithms are
used.
The addressed issues are challenging problems and are substantial
for developing an audio-visual speech recognition framework that can
maximize the information gather about the words uttered and minimize
the impact of noise.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/3065 |
| Date | January 2007 |
| Creators | Makkook, Mustapha |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | 1133785 bytes, application/pdf |
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