Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Several accents of English are spoken in South Africa. Automatic speech recognition (ASR) systems
should therefore be able to process the di erent accents of South African English (SAE).
In South Africa, however, system development is hampered by the limited availability of speech
resources. In this thesis we consider di erent acoustic modelling approaches and system con gurations
in order to determine which strategies take best advantage of a limited corpus of the ve
accents of SAE for the purpose of ASR. Three acoustic modelling approaches are considered:
(i) accent-speci c modelling, in which accents are modelled separately; (ii) accent-independent
modelling, in which acoustic training data is pooled across accents; and (iii) multi-accent modelling,
which allows selective data sharing between accents. For the latter approach, selective
sharing is enabled by extending the decision-tree state clustering process normally used to construct
tied-state hidden Markov models (HMMs) by allowing accent-based questions.
In a rst set of experiments, we investigate phone and word recognition performance achieved
by the three modelling approaches in a con guration where the accent of each test utterance is
assumed to be known. Each utterance is therefore presented only to the matching model set.
We show that, in terms of best recognition performance, the decision of whether to separate
or to pool training data depends on the particular accents in question. Multi-accent acoustic
modelling, however, allows this decision to be made automatically in a data-driven manner.
When modelling the ve accents of SAE, multi-accent models yield a statistically signi cant
improvement of 1.25% absolute in word recognition accuracy over accent-speci c and accentindependent
models.
In a second set of experiments, we consider the practical scenario where the accent of each test
utterance is assumed to be unknown. Each utterance is presented simultaneously to a bank
of recognisers, one for each accent, running in parallel. In this setup, accent identi cation is
performed implicitly during the speech recognition process. A system employing multi-accent
acoustic models in this parallel con guration is shown to achieve slightly improved performance
relative to the con guration in which the accents are known. This demonstrates that accent
identi cation errors made during the parallel recognition process do not a ect recognition performance.
Furthermore, the parallel approach is also shown to outperform an accent-independent
system obtained by pooling acoustic and language model training data.
In a nal set of experiments, we consider the unsupervised reclassi cation of training set accent
labels. Accent labels are assigned by human annotators based on a speaker's mother-tongue or
ethnicity. These might not be optimal for modelling purposes. By classifying the accent of each
utterance in the training set by using rst-pass acoustic models and then retraining the models,
reclassi ed acoustic models are obtained. We show that the proposed relabelling procedure does
not lead to any improvements and that training on the originally labelled data remains the best
approach. / AFRIKAANSE OPSOMMING: Verskeie aksente van Engels word in Suid Afrika gepraat. Outomatiese spraakherkenningstelsels
moet dus in staat wees om verskillende aksente van Suid Afrikaanse Engels (SAE) te kan
hanteer. In Suid Afrika word die ontwikkeling van spraakherkenningstegnologie egter deur die
beperkte beskikbaarheid van geannoteerde spraakdata belemmer. In hierdie tesis ondersoek ons
verskillende akoestiese modelleringstegnieke en stelselkon gurasies ten einde te bepaal watter
strategie e die beste gebruik maak van 'n databasis van die vyf aksente van SAE. Drie akoestiese
modelleringstegnieke word ondersoek: (i) aksent-spesi eke modellering, waarin elke aksent
apart gemodelleer word; (ii) aksent-onafhanklike modellering, waarin die akoestiese afrigdata
van verskillende aksente saamgegooi word; en (iii) multi-aksent modellering, waarin data selektief
tussen aksente gedeel word. Vir laasgenoemde word selektiewe deling moontlik gemaak
deur die besluitnemingsboom-toestandbondeling-algoritme, wat gebruik word in die afrig van
gebinde-toestand verskuilde Markov-modelle, uit te brei deur aksent-gebaseerde vrae toe te laat.
In 'n eerste stel eksperimente word die foon- en woordherkenningsakkuraathede van die drie modelleringstegnieke
vergelyk in 'n kon gurasie waarin daar aanvaar word dat die aksent van elke
toetsspraakdeel bekend is. In hierdie kon gurasie word elke spraakdeel slegs gebied aan die
modelstel wat ooreenstem met die aksent van die spraakdeel. In terme van herkenningsakkuraathede,
wys ons dat die keuse tussen aksent-spesi eke en aksent-onafhanklike modellering
afhanklik is van die spesi eke aksente wat ondersoek word. Multi-aksent akoestiese modellering
stel ons egter in staat om hierdie besluit outomaties op 'n data-gedrewe wyse te neem. Vir
die modellering van die vyf aksente van SAE lewer multi-aksent modelle 'n statisties beduidende
verbetering van 1.25% absoluut in woordherkenningsakkuraatheid op in vergelyking met
aksent-spesi eke en aksent-onafhanklike modelle.
In 'n tweede stel eksperimente word die praktiese scenario ondersoek waar daar aanvaar word
dat die aksent van elke toetsspraakdeel onbekend is. Elke spraakdeel word gelyktydig gebied aan
'n stel herkenners, een vir elke aksent, wat in parallel hardloop. In hierdie opstelling word aksentidenti
kasie implisiet uitgevoer. Ons vind dat 'n stelsel wat multi-aksent akoestiese modelle
in parallel inspan, e ense verbeterde werkverrigting toon in vergelyking met die opstelling waar
die aksent bekend is. Dit dui daarop dat aksentidenti seringsfoute wat gemaak word gedurende
herkenning, nie werkverrigting be nvloed nie. Verder wys ons dat die parallelle benadering ook
beter werkverrigting toon as 'n aksent-onafhanklike stelsel wat verkry word deur akoestiese en
taalmodelleringsafrigdata saam te gooi.
In 'n nale stel eksperimente ondersoek ons die ongekontroleerde herklassi kasie van aksenttoekennings
van die spraakdele in ons afrigstel. Aksente word gemerk deur menslike transkribeerders
op grond van 'n spreker se moedertaal en ras. Hierdie toekennings is nie noodwendig
optimaal vir modelleringsdoeleindes nie. Deur die aksent van elke spraakdeel in die afrigstel te
klassi seer deur van aanvanklike akoestiese modelle gebruik te maak en dan weer modelle af te
rig, word hergeklassi seerde akoestiese modelle verkry. Ons wys dat die voorgestelde herklassi
seringsalgoritme nie tot enige verbeterings lei nie en dat dit die beste is om modelle op die
oorspronklike data af te rig.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/20249 |
| Date | 03 1900 |
| Creators | Kamper, Herman |
| Contributors | Niesler, T. R., Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 124 p. : ill. |
| Rights | Stellenbosch University |
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