Implementing a distributed approach for speech resource and system development / Nkadimeng Raymond Molapo

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 h...

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Main Author: Molapo, Nkadimeng Raymond
Language:en
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10394/15922
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spelling ndltd-NWUBOLOKA1-oai-dspace.nwu.ac.za-10394-159222016-03-16T04:01:28ZImplementing a distributed approach for speech resource and system development / Nkadimeng Raymond MolapoMolapo, Nkadimeng RaymondAutomatic speech recognitionSmart phonesTranscribed speech corporaUnder-resourced languageGoogle App EngineData verificationShonaThe 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, 20142016-01-19T09:59:37Z2016-01-19T09:59:37Z2014Thesishttp://hdl.handle.net/10394/15922en
collection NDLTD
language en
sources NDLTD
topic Automatic speech recognition
Smart phones
Transcribed speech corpora
Under-resourced language
Google App Engine
Data verification
Shona
spellingShingle Automatic speech recognition
Smart phones
Transcribed speech corpora
Under-resourced language
Google App Engine
Data verification
Shona
Molapo, Nkadimeng Raymond
Implementing a distributed approach for speech resource and system development / Nkadimeng Raymond Molapo
description 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
author Molapo, Nkadimeng Raymond
author_facet Molapo, Nkadimeng Raymond
author_sort Molapo, Nkadimeng Raymond
title Implementing a distributed approach for speech resource and system development / Nkadimeng Raymond Molapo
title_short Implementing a distributed approach for speech resource and system development / Nkadimeng Raymond Molapo
title_full Implementing a distributed approach for speech resource and system development / Nkadimeng Raymond Molapo
title_fullStr Implementing a distributed approach for speech resource and system development / Nkadimeng Raymond Molapo
title_full_unstemmed Implementing a distributed approach for speech resource and system development / Nkadimeng Raymond Molapo
title_sort implementing a distributed approach for speech resource and system development / nkadimeng raymond molapo
publishDate 2016
url http://hdl.handle.net/10394/15922
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