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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/4170 |
| Date | 03 1900 |
| Creators | Nkadimeng, Calvin |
| Contributors | Niesler, T. R., 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 | Unknown |
| Type | Thesis |
| Format | 70 p. : ill. |
| Rights | University of Stellenbosch |
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