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Spoken language identification in resource-scarce environments
Dissertation (MEng)--University of Pretoria, 2010.
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University of Pretoria
2013
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| _version_ | 1867613707675107328 |
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| access_status_str | Open Access |
| author2 | Davel, Marelie Hattingh |
| author_browse | Davel, Marelie Hattingh |
| author_facet | Davel, Marelie Hattingh |
| collection | Thesis |
| dc_rights_str_mv | © 2009, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng)--University of Pretoria, 2010. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/27513 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:40:25.890Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/27513 Spoken language identification in resource-scarce environments Davel, Marelie Hattingh mariuspeche@gmail.con Peche, Marius Taal modellering Parallelle foneem herkenning Outomatiese spraak herkenning Gesproke taal identifisering Menslike taal tegnologie Suboptimal resources Mismatched resources Incomplete resources Language modeling Parallel phoneme recognition Automatic speech recognition Human language technologies Spoken language identification Onvolledige hulpbronne Teenstrydige hulpbronne Ondergeskikte hulpbronne UCTD Dissertation (MEng)--University of Pretoria, 2010. South Africa has eleven official languages, ten of which are considered “resource-scarce”. For these languages, even basic linguistic resources required for the development of speech technology systems can be difficult or impossible to obtain. In this thesis, the process of developing Spoken Language Identification (S-LID) systems in resource-scarce environments is investigated. A Parallel Phoneme Recognition followed by Language Modeling (PPR-LM) architecture is utilized and three specific scenarios are investigated: (1) incomplete resources, including the lack of audio transcriptions and/or pronunciation dictionaries; (2) inconsistent resources, including the use of speech corpora that are unmatched with regard to domain or channel characteristics; and (3) poor quality resources, such as wrongly labeled or poorly transcribed data. Each situation is analysed, techniques defined to mitigate the effect of limited or poor quality resources, and the effectiveness of these techniques evaluated experimentally. Techniques evaluated include the development of orthographic tokenizers, bootstrapping of transcriptions, filtering of low quality audio, diarization and channel normalization techniques, and the human verification of miss-classified utterances. The knowledge gained from this research is used to develop the first S-LID system able to distinguish between all South African languages. The system performs well, able to differentiate among the eleven languages with an accuracy of above 67%, and among the six primary South African language families with an accuracy of higher than 80%, on segments of speech of between 2s and 10s in length. AFRIKAANS : Suid-Afrika het elf amptelike tale waarvan tien as hulpbron-skaars beskou word. Vir die tien tale kan selfs die basiese hulpbronne wat benodig word om spraak tegnologie stelsels te ontwikkel moeilik wees om te bekom. Die proses om ‘n Gesproke Taal Identifisering stelsel vir hulpbron-skaars omgewings te ontwikkel, word in hierdie tesis ondersoek. ‘n Parallelle Foneem Herkenning gevolg deur Taal Modellering argitektuur word ingespan om drie spesifieke moontlikhede word ondersoek: (1) Onvolledige Hulpbronne, byvoorbeeld vermiste transkripsies en uitspraak woordeboeke; (2) Teenstrydige Hulpbronne, byvoorbeeld die gebruik van spraak data-versamelings wat teenstrydig is in terme van kanaal kenmerke; en (3) Hulpbronne van swak kwaliteit, byvoorbeeld foutief geklasifiseerde data en klank opnames wat swak getranskribeer is. Elke situasie word geanaliseer, tegnieke om die negatiewe effekte van min of swak hulpbronne te verminder word ontwikkel, en die bruikbaarheid van hierdie tegnieke word deur middel van eksperimente bepaal. Tegnieke wat ontwikkel word sluit die ontwikkeling van ortografiese ontleders, die outomatiese ontwikkeling van nuwe transkripsies, die filtrering van swak kwaliteit klank-data, klank-verdeling en kanaal normalisering tegnieke, en menslike verifikasie van verkeerd geklassifiseerde uitsprake in. Die kennis wat deur hierdie navorsing bekom word, word gebruik om die eerste Gesproke Taal Identifisering stelsel wat tussen al die tale van Suid-Afrika kan onderskei, te ontwikkel. Hierdie stelsel vaar relatief goed, en kan die elf tale met ‘n akkuraatheid van meer as 67% identifiseer. Indien daar op die ses taal families gefokus word, verbeter die persentasie tot meer as 80% vir segmente wat tussen 2 en 10 sekondes lank. Copyright Electrical, Electronic and Computer Engineering unrestricted 2013-09-07T11:43:12Z 2010-08-24 2013-09-07T11:43:12Z 2010-04-14 2010-08-24 2010-08-24 Dissertation Peche, M 2009, Spoken language identification in resource-scarce environments, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/27513 > E10/455/gm http://hdl.handle.net/2263/27513 http://upetd.up.ac.za/thesis/available/etd-08242010-191214/ © 2009, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Taal modellering Parallelle foneem herkenning Outomatiese spraak herkenning Gesproke taal identifisering Menslike taal tegnologie Suboptimal resources Mismatched resources Incomplete resources Language modeling Parallel phoneme recognition Automatic speech recognition Human language technologies Spoken language identification Onvolledige hulpbronne Teenstrydige hulpbronne Ondergeskikte hulpbronne UCTD Spoken language identification in resource-scarce environments |
| title | Spoken language identification in resource-scarce environments |
| title_full | Spoken language identification in resource-scarce environments |
| title_fullStr | Spoken language identification in resource-scarce environments |
| title_full_unstemmed | Spoken language identification in resource-scarce environments |
| title_short | Spoken language identification in resource-scarce environments |
| title_sort | spoken language identification in resource scarce environments |
| topic | Taal modellering Parallelle foneem herkenning Outomatiese spraak herkenning Gesproke taal identifisering Menslike taal tegnologie Suboptimal resources Mismatched resources Incomplete resources Language modeling Parallel phoneme recognition Automatic speech recognition Human language technologies Spoken language identification Onvolledige hulpbronne Teenstrydige hulpbronne Ondergeskikte hulpbronne UCTD |
| url | http://hdl.handle.net/2263/27513 http://upetd.up.ac.za/thesis/available/etd-08242010-191214/ |