Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / In this thesis we studied large-vocabulary continuous speech recognition.
We considered the components necessary to realise a large-vocabulary speech
recogniser and how systems such as Sphinx and HTK solved the problems
facing such a system.
Hidden Markov Models (HMMs) have been a common approach to
acoustic modelling in speech recognition in the past. HMMs are well suited
to modelling speech, since they are able to model both its stationary nature
and temporal e ects. We studied HMMs and the algorithms associated with
them. Since incorporating all knowledge sources as e ciently as possible is
of the utmost importance, the N-Best paradigm was explored along with
some more advanced HMM algorithms.
The way in which sounds and words are constructed has been studied
extensively in the past. Context dependency on the acoustic level and on
the linguistic level can be exploited to improve the performance of a speech recogniser. We considered some of the techniques used in the past to solve
the associated problems.
We implemented and combined some chosen algorithms to form our
system and reported the recognition results. Our nal system performed
reasonably well and will form an ideal framework for future studies on
large-vocabulary speech recognition at the University of Stellenbosch. Many
avenues of research for future versions of the system were considered.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/4050 |
| Date | 03 1900 |
| Creators | Swart, Ranier Adriaan |
| Contributors | Du Preez, J. A., University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Stellenbosch |
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