Municipal leaders in the United States face difficult decisions when prioritizing nonmandated civic projects for funding, especially when operating budgets are restricted. This phenomenological study investigated municipal leaders' decision-making processes in a state in the southern United States, using a conceptual framework based on rational choice theory, bounded rationality, and group decision-making theory. It specifically explored personal and organizational decision-making processes related to the prioritization and funding of nonmandated civic projects via in-depth interviews with a convenience sample of 15 municipal leaders. Thematic analysis identified expert opinions, the time and cost to complete a project, the perceived value relative to expense, and the availability of additional funding sources as themes important to understanding participants' decision-making processes. Organizational factors that were important in these decisions included the need for clearly defined responsibilities and consistency in funding decisions. No clearly defined organizational processes were in place in any of the participants' municipalities, and the participants noted that areas such as infrastructure improvements, traffic congestion, community involvement, and formal processes in their municipalities were in need of improvement. Positive social change can flow from greater governmental transparency through municipal decision makers' adoption of systematic decision-making systems and processes. Positive social change can also result from greater inclusiveness through increased public outreach efforts. Results add to the research base by contributing to a better theoretical understanding of organizational decision-making processes in the municipal context.

Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. In this thesis we present research into developing techniques and tools to (1) harvest audio data, (2) rapidly adapt ASR systems and (3) select “useful” training samples in order to assist with resource-scarce ASR system development. We demonstrate an automatic audio harvesting approach which efficiently creates a speech recognition corpus by harvesting an easily available audio resource. We show that by starting with bootstrapped acoustic models, trained with language data obtain from a dialect, and then running through a few iterations of an alignment-filter-retrain phase it is possible to create an accurate speech recognition corpus. As a demonstration we create a South African English speech recognition corpus by using our approach and harvesting an internet website which provides audio and approximate transcriptions. The acoustic models developed from harvested data are evaluated on independent corpora and show that the proposed harvesting approach provides a robust means to create ASR resources. As there are many acoustic model adaptation techniques which can be implemented by an ASR system developer it becomes a costly endeavour to select the best adaptation technique. We investigate the dependence of the adaptation data amount and various adaptation techniques by systematically varying the adaptation data amount and comparing the performance of various adaptation techniques. We establish a guideline which can be used by an ASR developer to chose the best adaptation technique given a size constraint on the adaptation data, for the scenario where adaptation between narrow- and wide-band corpora must be performed. In addition, we investigate the effectiveness of a novel channel normalisation technique and compare the performance with standard normalisation and adaptation techniques. Lastly, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequency-matched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions. / Thesis (PhD (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. In this thesis we present research into developing techniques and tools to (1) harvest audio data, (2) rapidly adapt ASR systems and (3) select “useful” training samples in order to assist with resource-scarce ASR system development. We demonstrate an automatic audio harvesting approach which efficiently creates a speech recognition corpus by harvesting an easily available audio resource. We show that by starting with bootstrapped acoustic models, trained with language data obtain from a dialect, and then running through a few iterations of an alignment-filter-retrain phase it is possible to create an accurate speech recognition corpus. As a demonstration we create a South African English speech recognition corpus by using our approach and harvesting an internet website which provides audio and approximate transcriptions. The acoustic models developed from harvested data are evaluated on independent corpora and show that the proposed harvesting approach provides a robust means to create ASR resources. As there are many acoustic model adaptation techniques which can be implemented by an ASR system developer it becomes a costly endeavour to select the best adaptation technique. We investigate the dependence of the adaptation data amount and various adaptation techniques by systematically varying the adaptation data amount and comparing the performance of various adaptation techniques. We establish a guideline which can be used by an ASR developer to chose the best adaptation technique given a size constraint on the adaptation data, for the scenario where adaptation between narrow- and wide-band corpora must be performed. In addition, we investigate the effectiveness of a novel channel normalisation technique and compare the performance with standard normalisation and adaptation techniques. Lastly, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequency-matched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions. / Thesis (PhD (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

Made available in DSpace on 2015-05-14T12:11:43Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2263703 bytes, checksum: 6699aaf8af680c11660636f37146f3bb (MD5) Previous issue date: 2011-10-10 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This research aims at investigating the possibilities of conceiving an aesthetic based ethics from the notion of creation within a nietzschean context. Such notion is clearly revisited and operated by the philosopher, along with the main figures of thought developed in his reflections, which is an important element for the understanding of human existence. From such standpoint, we conjecture to be possible a reconfiguration of the ethos based in the nietzschean sense of creation, of the creating (schaffen) as construction of values according with the cosmic-physiological activity (in accordance with the belated image of the will to power) and of the experimentation in frank correspondence with the opening of the tragic world, artistically affirmed as an existential aesthetic. Along the development of this hypothesis, the use of the expression creativity was chosen, with which it is believed to achieve a kind of creative human, as a concretization of this aesthetic-ethics, designated by the image of a human, scarce human . Such being, according to our understanding, would not be the man of resentment bound to metaphysics and morality, nor would it be the beyond-man (Übermensch), as outstripping experience or proposal to tackle with the whole vacuum of the meaning of life: the human, scarce human is right between the resentful man and the superhuman. It is argued that this rare creative man, as scarce human, can be found in some typological figures outlined by Nietzsche in his philosophical journey. By using a critic-analytical approach of nietzschean texts, along with extensive hermeneutical analyses, a diachronic identification of ideas, arguments and assertions that consolidate the referred hypothesis is sought to be achieved. The structure of this research is divided in four chapters. In the first, the notion of creation is analyzed in its nietzschean kernel conception, emphasizing its constant and fundamental emerging along with the conceptions developed by latter Nietzsche. In the second, the nietzschean arguments that discuss the creational activity, the concept of experience, the tragic knowledge and the imperative of life-affirming, as formational criteria of an alternative ethos to traditional ethics, are investigated. In the third, some of the issues left by interpreters that have dealt with the dimension assumed by art in Nietzsche s Philosophy are examined; comprising reasoning to stand behind the argument that creation is taken as quality of the will to power in its process of concretization, in a sense of artistic creation. In the last chapter, we investigate the Nietzschean indications of a possible human greatness connected to an active and creative typology, by analyzing of the type lord, aristocratic and noble as well as others figures constructed by the philosopher. In addition, the insertion of Nietzsche s reflections in the field of philosophical anthropology is questioned, pondering on the articulation between human condition and evaluation, which is a characteristic of his mature constructs. After such survey, the possibilities of this kind of human, taken here as creative, are discussed as such being creates values by expression of power as a simultaneously affirmative activity. / A presente pesquisa tem por objetivo investigar as possibilidades de conceber uma ética de caráter estético a partir da noção de criação no contexto filosófico nietzschiano. Tal noção é claramente revisitada e operada pelo filósofo junto às principais figuras de pensamento desenvolvidas em suas reflexões, constituindo importante elemento de compreensão da existência humana. Nessa direção, conjeturamos ser exequível uma reconfiguração do ethos baseada no sentido nietzschiano de criação, do criar (schaffen) enquanto construção de valores em função da atividade cósmico-fisiológica (conforme a imagem tardia da vontade de poder), da experimentação em franca correspondência com a abertura trágica do mundo, afirmada artisticamente como uma estética existencial. No desenvolvimento desta hipótese, optamos pelo uso da expressão criatividade, com a qual acreditamos depreender um tipo humano criativo como disposição concretizadora dessa estético-ética, designado pela imagem de um humano, escasso humano . Este, segundo entendemos, não seria o homem do ressentimento agrilhoado à metafísica e à moral, nem o além-do-homem (Übermensch), enquanto experiência de ultrapassagem ou proposta para lidar com o completo vácuo de sentido da vida: o humano, escasso humano está justamente entre o homem ressentido e o sobre-humano. Ponderamos que esse homem criativo, raro, porque escasso humano , mostra-se em algumas figuras tipológicas delineadas por Nietzsche em seu percurso filosófico. Utilizando um método crítico-analítico dos textos nietzschianos, acompanhado de intensa análise hermenêutica, buscamos identificar diacronicamente ideias, argumentos e asserções que consolidam a referida hipótese. A estrutura do trabalho está dividida em quatro capítulos. No primeiro, analisamos a noção de criação no cerne do pensamento nietzschiano, enfatizando seu constante e fundamental aparecimento junto às concepções desenvolvidas pelo Nietzsche tardio. No segundo, perscrutamos os argumentos nietzschianos que aduzem a atividade criadora, o conceito de experiência, o saber trágico, e o imperativo de afirmação da vida, enquanto critérios formadores de um ethos alternativo às éticas tradicionais. No terceiro, examinamos algumas questões deixadas por intérpretes que trataram da dimensão assumida pela arte na filosofia de Nietzsche, reunindo argumentações para defender que a criação é entendida como qualidade da vontade de poder em seu efetivar-se, no sentido da criação artística. No último capítulo, investigamos as indicações nietzschianas a uma possível grandeza humana ligada a uma tipologia ativa e criadora, mediante análise do tipo senhor, aristocrata e nobre, bem como de outras figuras construídas pelo filósofo. Nele, indagamos sobre a inserção das reflexões de Nietzsche no rol da antropologia filosófica, ponderando a articulação entre condição humana e avaliação, característica de seus construtos maduros. Após tal exame, discutimos as possibilidades deste tipo humano entendido por nós como criativo, na medida em que engendra valores por expressão de potência como atividade simultaneamente afirmativa.

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When working in the terrain of text processing, metadata about a particular text plays an important role. Metadata is often generated using automatic text classification systems which classifies a text into one or more predefined classes or categories based on its contents. One of the dimensions by which a text can be can be classified, is the genre of a text. In this study the development of an automatic genre classification system in a resource scarce environment is postulated. This study aims to: i) investigate the techniques and approaches that are generally used for automatic genre classification systems, and identify the best approach for Afrikaans (a resource scarce language), ii) transfer this approach to other indigenous South African resource scarce languages, and iii) investigate the effectiveness of technology recycling for closely related languages in a resource scarce environment. To achieve the first goal, five machine learning approaches were identified from the literature that are generally used for text classification, together with five common approaches to feature extraction. Two different approaches to the identification of genre classes are presented. The machine learning-, feature extraction- and genre class identification approaches were used in a series of experiments to identify the best approach for genre classification for a resource scarce language. The best combination is identified as the multinomial naïve Bayes algorithm, using a bag of words approach as features to classify texts into three abstract classes. This results in an f-score (performance measure) of 0.929 and it was subsequently shown that this approach can be successfully applied to other indigenous South African languages. To investigate the viability of technology recycling for genre classification systems for closely related languages, Dutch test data was classified using an Afrikaans genre classification system and it is shown that this approach works well. A pre-processing step was implemented by using a machine translation system to increase the compatibility between Afrikaans and Dutch by translating the Dutch texts before classification. This results in an f-score of 0.577, indicating that technology recycling between closely related languages has merit. This approach can be used to promote and fast track the development of genre classification systems in a resource scarce environment. / MA (Linguistics and Literary Theory), North-West University, Potchefstroom Campus, 2013

When working in the terrain of text processing, metadata about a particular text plays an important role. Metadata is often generated using automatic text classification systems which classifies a text into one or more predefined classes or categories based on its contents. One of the dimensions by which a text can be can be classified, is the genre of a text. In this study the development of an automatic genre classification system in a resource scarce environment is postulated. This study aims to: i) investigate the techniques and approaches that are generally used for automatic genre classification systems, and identify the best approach for Afrikaans (a resource scarce language), ii) transfer this approach to other indigenous South African resource scarce languages, and iii) investigate the effectiveness of technology recycling for closely related languages in a resource scarce environment. To achieve the first goal, five machine learning approaches were identified from the literature that are generally used for text classification, together with five common approaches to feature extraction. Two different approaches to the identification of genre classes are presented. The machine learning-, feature extraction- and genre class identification approaches were used in a series of experiments to identify the best approach for genre classification for a resource scarce language. The best combination is identified as the multinomial naïve Bayes algorithm, using a bag of words approach as features to classify texts into three abstract classes. This results in an f-score (performance measure) of 0.929 and it was subsequently shown that this approach can be successfully applied to other indigenous South African languages. To investigate the viability of technology recycling for genre classification systems for closely related languages, Dutch test data was classified using an Afrikaans genre classification system and it is shown that this approach works well. A pre-processing step was implemented by using a machine translation system to increase the compatibility between Afrikaans and Dutch by translating the Dutch texts before classification. This results in an f-score of 0.577, indicating that technology recycling between closely related languages has merit. This approach can be used to promote and fast track the development of genre classification systems in a resource scarce environment. / MA (Linguistics and Literary Theory), North-West University, Potchefstroom Campus, 2013

The rapid development of corpus-based speech systems such as concatenative synthesis systems for under-resourced languages requires an efficient, consistent and accurate solution with regard to phonetic speech segmentation. Manual development of phonetically annotated corpora is a time consuming and expensive process which suffers from challenges regarding consistency and reproducibility, while automation of this process has only been satisfactorily demonstrated on large corpora of a select few languages by employing techniques requiring extensive and specialised resources. In this work we considered the problem of phonetic segmentation in the context of developing small prototypical speech synthesis corpora for new under-resourced languages. This was done through an empirical evaluation of existing segmentation techniques on typical speech corpora in three South African languages. In this process, the performance of these techniques were characterised under different data conditions and the efficient application of these techniques were investigated in order to improve the accuracy of resulting phonetic alignments. We found that the application of baseline speaker-specific Hidden Markov Models results in relatively robust and accurate alignments even under extremely limited data conditions and demonstrated how such models can be developed and applied efficiently in this context. The result is segmentation of sufficient quality for synthesis applications, with the quality of alignments comparable to manual segmentation efforts in this context. Finally, possibilities for further automated refinement of phonetic alignments were investigated and an efficient corpus development strategy was proposed with suggestions for further work in this direction. / Thesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.

The rapid development of corpus-based speech systems such as concatenative synthesis systems for under-resourced languages requires an efficient, consistent and accurate solution with regard to phonetic speech segmentation. Manual development of phonetically annotated corpora is a time consuming and expensive process which suffers from challenges regarding consistency and reproducibility, while automation of this process has only been satisfactorily demonstrated on large corpora of a select few languages by employing techniques requiring extensive and specialised resources. In this work we considered the problem of phonetic segmentation in the context of developing small prototypical speech synthesis corpora for new under-resourced languages. This was done through an empirical evaluation of existing segmentation techniques on typical speech corpora in three South African languages. In this process, the performance of these techniques were characterised under different data conditions and the efficient application of these techniques were investigated in order to improve the accuracy of resulting phonetic alignments. We found that the application of baseline speaker-specific Hidden Markov Models results in relatively robust and accurate alignments even under extremely limited data conditions and demonstrated how such models can be developed and applied efficiently in this context. The result is segmentation of sufficient quality for synthesis applications, with the quality of alignments comparable to manual segmentation efforts in this context. Finally, possibilities for further automated refinement of phonetic alignments were investigated and an efficient corpus development strategy was proposed with suggestions for further work in this direction. / Thesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.