Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Language identification using Gaussian mixture models
Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Stellenbosch : University of Stellenbosch
2010
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867614096842555392 |
|---|---|
| access_status_str | Open Access |
| author | Nkadimeng, Calvin |
| author2 | Niesler, T. R. |
| author_browse | Niesler, T. R. Nkadimeng, Calvin |
| author_facet | Niesler, T. R. Nkadimeng, Calvin |
| author_sort | Nkadimeng, Calvin |
| collection | Thesis |
| dc_rights_str_mv | University of Stellenbosch |
| description | Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/4170 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:46:36.532Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2010 |
| publishDateRange | 2010 |
| publishDateSort | 2010 |
| publisher | Stellenbosch : University of Stellenbosch |
| publisherStr | Stellenbosch : University of Stellenbosch |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/4170 Language identification using Gaussian mixture models Nkadimeng, Calvin Niesler, T. R. University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Automatic language identification Gaussian mixture models Southern African languages Speech recognition Dissertations -- Electronic engineering Theses -- Electronic engineering Speech processing systems Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. ENGLISH ABSTRACT: The importance of Language Identification for African languages is seeing a dramatic increase due to the development of telecommunication infrastructure and, as a result, an increase in volumes of data and speech traffic in public networks. By automatically processing the raw speech data the vital assistance given to people in distress can be speeded up, by referring their calls to a person knowledgeable in that language. To this effect a speech corpus was developed and various algorithms were implemented and tested on raw telephone speech data. These algorithms entailed data preparation, signal processing, and statistical analysis aimed at discriminating between languages. The statistical model of Gaussian Mixture Models (GMMs) were chosen for this research due to their ability to represent an entire language with a single stochastic model that does not require phonetic transcription. Language Identification for African languages using GMMs is feasible, although there are some few challenges like proper classification and accurate study into the relationship of langauges that need to be overcome. Other methods that make use of phonetically transcribed data need to be explored and tested with the new corpus for the research to be more rigorous. AFRIKAANSE OPSOMMING: Die belang van die Taal identifiseer vir Afrika-tale is sien ’n dramatiese toename te danke aan die ontwikkeling van telekommunikasie-infrastruktuur en as gevolg ’n toename in volumes van data en spraak verkeer in die openbaar netwerke.Deur outomaties verwerking van die ruwe toespraak gegee die noodsaaklike hulp verleen aan mense in nood kan word vinniger-up ”, deur te verwys hul oproepe na ’n persoon ingelichte in daardie taal. Tot hierdie effek van ’n toespraak corpus het ontwikkel en die verskillende algoritmes is gemplementeer en getoets op die ruwe telefoon toespraak gegee.Hierdie algoritmes behels die data voorbereiding, seinverwerking, en statistiese analise wat gerig is op onderskei tussen tale.Die statistiese model van Gauss Mengsel Modelle (GGM) was gekies is vir hierdie navorsing as gevolg van hul vermo te verteenwoordig ’n hele taal met’ n enkele stogastiese model wat nodig nie fonetiese tanscription nie. Taal identifiseer vir die Afrikatale gebruik GGM haalbaar is, alhoewel daar enkele paar uitdagings soos behoorlike klassifikasie en akkurate ondersoek na die verhouding van TALE wat moet oorkom moet word.Ander metodes wat gebruik maak van foneties getranskribeerde data nodig om ondersoek te word en getoets word met die nuwe corpus vir die ondersoek te word strenger. 2010-02-19T09:10:30Z 2010-08-13T14:59:49Z 2010-02-19T09:10:30Z 2010-08-13T14:59:49Z 2010-03 Thesis http://hdl.handle.net/10019.1/4170 en University of Stellenbosch 70 p. : ill. application/pdf Stellenbosch : University of Stellenbosch |
| spellingShingle | Automatic language identification Gaussian mixture models Southern African languages Speech recognition Dissertations -- Electronic engineering Theses -- Electronic engineering Speech processing systems Nkadimeng, Calvin Language identification using Gaussian mixture models |
| title | Language identification using
Gaussian mixture models |
| title_full | Language identification using
Gaussian mixture models |
| title_fullStr | Language identification using
Gaussian mixture models |
| title_full_unstemmed | Language identification using
Gaussian mixture models |
| title_short | Language identification using
Gaussian mixture models |
| title_sort | language identification using gaussian mixture models |
| topic | Automatic language identification Gaussian mixture models Southern African languages Speech recognition Dissertations -- Electronic engineering Theses -- Electronic engineering Speech processing systems |
| url | http://hdl.handle.net/10019.1/4170 |
| work_keys_str_mv | AT nkadimengcalvin languageidentificationusinggaussianmixturemodels |