The range of applications for high-quality automatic speech recognition (ASR) systems has grown
dramatically with the advent of smart phones, in which speech recognition can greatly enhance the
user experience. Currently, the languages with extensive ASR support on these devices are languages
that have thousands of hours of transcribed speech corpora already collected. Developing a speech
system for such a language is made simpler because extensive resources already exist. However for
languages that are not as prominent, the process is more difficult. Many obstacles such as reliability
and cost have hampered progress in this regard, and various separate tools for every stage of the
development process have been developed to overcome these difficulties.
Developing a system that is able to combine these identified partial solutions, involves customising
existing tools and developing new ones to interface the overall end-to-end process. This work documents
the integration of several tools to enable the end-to-end development of an Automatic Speech
Recognition system in a typical under-resourced language. Google App Engine is employed as the
core environment for data verification, storage and distribution, and used in conjunction with existing
tools for gathering text data and for speech data recording. We analyse the data acquired by each of
the tools and develop an ASR system in Shona, an important under-resourced language of Southern
Africa. Although unexpected logistical problems complicated the process, we were able to collect
a useable Shona speech corpus, and develop the first Automatic Speech Recognition system in that
language. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2014
| Identifer | oai:union.ndltd.org:NWUBOLOKA1/oai:dspace.nwu.ac.za:10394/15922 |
| Date | January 2014 |
| Creators | Molapo, Nkadimeng Raymond |
| Source Sets | North-West University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0031 seconds