Typescript. Includes bibliographical references (p. 321-325).

Project (Engin.)--George Mason University, 2008. / Vita: p. 94. Project director: Mohan Venigalla. Submitted in partial fulfillment of the requirements for the degree of Engineer Degree. Title from PDF t.p. (viewed Oct. 13, 2008). Includes bibliographical references (p. 93). Also issued in print.

Please read the abstract in the section, 00front, of this document / Dissertation (MA (AAC))--University of Pretoria, 2008. / Centre for Augmentative and Alternative Communication (CAAC) / unrestricted

Thesis (M. Sc. (Computer Science)) -- University of Limpopo, 2021 / The task of automatic speaker recognition, wherein a system verifies or identifies speakers from a recording of their voices, has been researched for several decades. However, research in this area has been carried out largely on freely accessible speaker datasets built on languages that are well-resourced like English. This study undertakes automatic speaker recognition research focused on a low-resourced language, Sepedi. As one of the 11 official languages in South Africa, Sepedi is spoken by at least 2.8 million people. Pre-recorded voices were acquired from a speech and language national repository, namely, the National Centre for Human Language Technology (NCHLT), were we selected the Sepedi NCHLT Speech Corpus. The open-source pyAudioAnalysis python library was used to extract three types of acoustic features of speech namely, time, frequency and cepstral domain features, from the acquired speech data. The effects and compatibility of these acoustic features was investigated. It was observed that combining the three acoustic features of speech had a more significant effect than using individual features as far as speaker recognition accuracy is concerned. The study also investigated the performance of machine learning algorithms on low-resourced languages such as Sepedi. Five machine learning (ML) algorithms implemented on Scikit-learn namely, K-nearest neighbours (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), and multi-layer perceptrons (MLP) were used to train different classifier models. The GridSearchCV algorithm, also implemented on Scikit-learn, was used to deduce ideal hyper-parameters for each of the five ML algorithms. The classifier models were evaluated on recognition accuracy and the results show that the MLP classifier, with a recognition accuracy of 98%, outperforms KNN, RF, LR and SVM classifiers. A graphical user interface (GUI) is developed and the best performing classifier model, MLP, is deployed on the developed GUI intended to be used for real time speaker identification and verification tasks. Participants were recruited to the GUI performance and acceptable results were obtained

Thesis (M. Log.)--Universiteit van Pretoria, 2000. / Summary in English and Afrikaans. Includes bibliographical references.