This study examines how second language (L2) English learners differ in their amount of language anxiety across three communication tasks: a formal academic speaking test, an informal casual interaction with a classmate during an activity in pairs, and a formal interview with an unfamiliar native speaker. It also addressed whether and how their language anxiety across the three tasks affected their accuracy, comprehensibility, and rate of speech. Thirty-seven intermediate-high L2 English learners participated in the three communication tasks. They also completed a general language anxiety questionnaire and three anxiety inventories--one before each task. Results demonstrated that the speaking exam caused more anxiety than the other two tasks. This anxiety had a positive correlation with both accuracy and comprehensibility, but not speech rate. The anxiety caused by the native speaker activity was found to have a correlation with comprehensibility, but nothing else. Finally, the anxiety caused by the pair activity did not have a significant relationship with any variable. It was also found that the participants' speech rate was higher in the native speaker activity than the pair activity, but that there was no significant difference in speech rate, or any other factor, between the exam and the other two activities. The results of the current study suggest that anxiety has a demonstrable effect on the way people speak. Higher anxiety was more likely to produce significant effects on one or more aspects of the participants' speech, including more comprehensible and accurate speech.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The assessment of oral proficiency forms an important part of learning a second language. However, the manual assessment of oral proficiency is a labour intensive task requiring specific expertise. An automatic assessment system can reduce the cost and workload associated with this task. Although such systems are available, they are typically aimed towards assessing students of American or British English, making them poorly suited for speakers of South African English. Additionally, most research in this field is focussed on the assessment of foreign language students, while we investigate the assessment of second language students. These students can be expected to have more advanced skills in the target language than foreign language speakers. This thesis presents a number of scoring algorithms for the automatic assessment of oral proficiency. Experiments were conducted on a corpus of responses recorded during an automated oral test. These responses were rated for proficiency by a panel of raters based on five different rating scales. Automatic scoring algorithms were subsequently applied to the same utterances and their correlations with the human ratings determined. In contrast to the findings of other researchers, posterior likelihood scores were found to be ineffective as an indicator of proficiency for the corpus used in this study. Four different segmentation based algorithms were shown to be moderately correlated with human ratings, while scores based on the accuracy of a repeated prompt were found to be well correlated with human assessments. Finally, multiple linear regression was used to combine different scoring algorithms to predict human assessments. The correlations between human ratings and these score combinations ranged between 0.52 and 0.90. / AFRIKAANSE OPSOMMING: Die assessering van spraakvaardigheid is ’n belangrike komponent van die aanleer van ’n tweede taal. Die praktiese uitvoer van sodanige assessering is egter ’n arbeids-intensiewe taak wat spesifieke kundigheid vereis. Die gebruik van ’n outomatiese stelsel kan die koste en werkslading verbonde aan die assessering van ’n groot aantal studente drasties verminder. Hoewel sulke stelsels beskikbaar is, is dit tipies gemik op die assessering van studente wat Amerikaanse of Britse Engels wil aanleer, en is dus nie geskik vir sprekers van Suid Afrikaanse Engels nie. Verder is die meerderheid navorsing op hierdie gebied gefokus op die assessering van vreemde-taal sprekers, terwyl hierdie tesis die assessering van tweede-taal sprekers ondersoek. Dit is te wagte dat hierdie sprekers se spraakvaardighede meer gevorderd sal wees as di´e van vreemde-taal sprekers. Hierdie tesis behandel ’n aantal evaluasie-algoritmes vir die outomatiese assessering van spraakvaardighede. Die eksperimente is uitgevoer op ’n stel opnames van studente se antwoorde op ’n outomatiese spraaktoets. ’n Paneel van menslike beoordelaars het hierdie opnames geassesseer deur gebruik te maak van vyf verskillende punteskale. Dieselfde opnames is deur die outomatiese evaluasie-algoritmes verwerk, en die korrelasies tussen die beoordelaars se punte en die outomatiese evaluerings is bepaal. In kontras met die bestaande navorsing, is daar gevind dat posterieure waarskynlikheidsalgoritmes nie ’n goeie aanduiding van spraakvaardighede gee vir ons datastel nie. Vier algoritmes wat van segmentasies gebruik maak, is ook ondersoek. Die evaluerings van hierdie algoritmes het redelike korrelasie getoon met die punte wat deur die beoordelaars toegeken is. Voorts is drie algoritmes ondersoek wat daarop gemik is om die akkuraatheid van herhaalde sinne te bepaal. Die evaluerings van hierdie algoritmes het goed gekorreleer met die punte wat deur die beoordelaars toegeken is. Laastens is liniˆere regressie gebruik om verskillende outomatiese evaluerings te kombineer en sodoende beoordelaars se punte te voorspel. Die korrelasies tussen hierdie kombinasies en die punte wat deur beoordelaars toegeken is, het gewissel tussen 0.52 en 0.90.

This master thesis concerns with emotional states and gender recognition on the basis of speech signal analysis. We used various prosodic and cepstral features for the description of the speech signal. In the text we describe non-invasive methods for glottal pulses estimation. The described features of speech were implemented in MATLAB. For their classification we used the GMM classifier, which uses the Gaussian probability distribution for modeling a feature space. Furthermore, we constructed a system for recognition of emotional states of the speaker and a system for gender recognition from speech. We tested the success of created systems with several features on speech signal segments of various lengths and compared the results. In the last part we tested the influence of speaker and gender on the success of emotional states recognition.