Thesis advisor: Mary E. Walsh / The out-of-school factors that low-income children face can impact their wellbeing and ability to learn (Rothstein, 2010), leading to low academic performance, and, in turn, high levels of stress among their teachers. One of the numerous potential strategies that exist to address this problem is the implementation of systemic student support interventions, which are hypothesized to support teachers in addition to students (Ball & Anderson-Butcher, 2014). Using the City Connects intervention as an example of a comprehensive, systemic student support intervention that has demonstrated positive effects for students, this dissertation study examines the impact of this same intervention on teachers. The study used data obtained from annual surveys administered to all teachers in public schools (across several districts) where the City Connects intervention was implemented. The sample consisted of 656 responses from teachers in Boston Public Schools that were participating in the City Connects intervention during three designated school years (2012-13, 2013-14, and 2015-16). The study implemented a mixed-methods approach that allowed for an in-depth analysis of teachers’ perceptions of support from the City Connects intervention through both quantitative and qualitative data sources. Following exploratory and confirmatory factor analyses of the survey instrument itself, survey data was analyzed using the Validating Quantitative Data Model (Creswell & Plano Clark, 2007), in which quantitative findings were confirmed and expanded upon through analysis of data from a small number of open-ended survey questions. The results of this dissertation study support the continued use of systemic student support interventions in schools, as data confirm that teachers in schools with City Connects report being supported by the intervention. These results hold for veteran teachers who have been teaching for over sixteen years and have participated in the City Connects intervention for over ten years. Ultimately, the findings of this dissertation study suggest that, in addition to leading to positive changes in students’ developmental trajectories, student support interventions can improve the experiences of other members of the school community. / Thesis (PhD) — Boston College, 2018. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.

Speech technologies such as text-to-speech synthesis (TTS) and automatic speech recognition (ASR) have recently generated much interest in the developed world as a user-interface medium to smartphones [1, 2]. However, it is also recognised that these technologies may potentially have a positive impact on the lives of those in the developing world, especially in Africa, by presenting an important medium for access to information where illiteracy and a lack of infrastructure play a limiting role [3, 4, 5, 6]. While these technologies continually experience important advances that keep extending their applicability to new and under-resourced languages, one particular area in need of further development is speech synthesis of African tone languages [7, 8]. The main objective of this work is acoustic modelling and synthesis of tone for an African tone,language: Yorùbá. We present an empirical investigation to establish the acoustic properties of tone in Yorùbá, and to evaluate resulting models integrated into a Hidden Markov model-based (HMMbased) TTS system. We show that in Yorùbá, which is considered a register tone language, the realisation of tone is not solely determined by pitch levels, but also inter-syllable and intra-syllable pitch dynamics. Furthermore, our experimental results indicate that utterance-wide pitch patterns are not only a result of cumulative local pitch changes (terracing), but do contain a significant gradual declination component. Lastly, models based on inter- and intra-syllable pitch dynamics using underlying linear pitch targets are shown to be relatively efficient and perceptually preferable to the current standard approach in statistical parametric speech synthesis employing HMM pitch models based on context-dependent phones. These findings support the applicability of the proposed models in under-resourced conditions. / PhD (Information Technology), North-West University, Vaal Triangle Campus, 2014

Speech technologies such as text-to-speech synthesis (TTS) and automatic speech recognition (ASR) have recently generated much interest in the developed world as a user-interface medium to smartphones [1, 2]. However, it is also recognised that these technologies may potentially have a positive impact on the lives of those in the developing world, especially in Africa, by presenting an important medium for access to information where illiteracy and a lack of infrastructure play a limiting role [3, 4, 5, 6]. While these technologies continually experience important advances that keep extending their applicability to new and under-resourced languages, one particular area in need of further development is speech synthesis of African tone languages [7, 8]. The main objective of this work is acoustic modelling and synthesis of tone for an African tone,language: Yorùbá. We present an empirical investigation to establish the acoustic properties of tone in Yorùbá, and to evaluate resulting models integrated into a Hidden Markov model-based (HMMbased) TTS system. We show that in Yorùbá, which is considered a register tone language, the realisation of tone is not solely determined by pitch levels, but also inter-syllable and intra-syllable pitch dynamics. Furthermore, our experimental results indicate that utterance-wide pitch patterns are not only a result of cumulative local pitch changes (terracing), but do contain a significant gradual declination component. Lastly, models based on inter- and intra-syllable pitch dynamics using underlying linear pitch targets are shown to be relatively efficient and perceptually preferable to the current standard approach in statistical parametric speech synthesis employing HMM pitch models based on context-dependent phones. These findings support the applicability of the proposed models in under-resourced conditions. / PhD (Information Technology), North-West University, Vaal Triangle Campus, 2014

Philosophiae Doctor - PhD / South Africa is committed to accelerating the roll-out of information and communication technologies (ICTs) to support development at all levels. E-inclusion intermediaries (e-IIs) are used in the country to bridge the digital divide and to create equal opportunities for citizens to benefit from using ICTs. E-IIs are established mainly in under-resourced communities by private, public and third-sector organisations to provide physical access to ICT services for free or at a very low cost. The aim of e-IIs is to make ICT services affordable for and accessible to marginalised and poor community members, who can use the ICT to support community development. The debate is ongoing regarding the contribution of e-IIs towards community development due to, in part, the lack of quantifiable evidence to support the impact that the e-IIs have on development in the communities. Furthermore, despite the existence of e-IIs in communities, there still are community members who do not use the e-IIs. This has been attributed to the lack of awareness of the e-IIs and the services they provide. This lack of awareness is often blamed on the ineffective communication strategies of e-IIs. E-IIs are accused of relying heavily on traditional communication channels and conventional mass media, which do not share information and create awareness effectively in the communities. The increased uptake of modern technologies, such as the Internet and mobile devices, in South Africa has created new opportunities to communicate with community members to share information and create awareness. Social media, for instance, which are mostly accessed through mobile devices, have made communication more accessible and inexpensive for community members with limited skills and resources. Social media have also become popular among development actors in their attempt to direct policy, create awareness and garner community members’ support for development interventions. Arguably, e-IIs could also benefit from using social media, which have become popular in some communities, to communicate with community members in order to create awareness of the e-IIs, the services they provide and the benefits of using ICTs to support community development. The investigation undertaken in this study was twofold. Firstly, the quick-scan analysis method was used to analyse fifty e-IIs. Using this method it was possible to explore the services that are provided by e-IIs as well as how e-IIs communicate with community members and other development actors. Secondly, using six in-depth case studies this study further investigated how e-IIs’ services support community development and how the e-IIs communicate for development, paying special attention to their use of social media.

Thesis (M.Sc. (Computer Science)) -- University of Limpopo, 2015 / This study investigates the potential of using graphemes, instead of phonemes, as acoustic sub-word units for monolingual and cross-lingual speech recognition for some of the under-resourced languages of the Limpopo Province, namely, IsiNdebele, Sepedi and Tshivenda. The performance of a grapheme-based recognition system is compared to that of phoneme-based recognition system. For each selected under-resourced language, automatic speech recognition (ASR) system based on the use of hidden Markov models (HMMs) was developed using both graphemes and phonemes as acoustic sub-word units. The ASR framework used models emission distributions by 16 Gaussian Mixture Models (GMMs) with 2 mixture increments. A third-order n-gram language model was used in all experiments. Identical speech datasets were used for each experiment per language. The LWAZI speech corpora and the National Centre for Human Language Technologies (NCHLT) speech corpora were used for training and testing the tied-state context-dependent acoustic models. The performance of all systems was evaluated at the word-level recognition using word error rate (WER). The results of our study show that grapheme-based continuous speech recognition, which copes with the problem of low-quality or unavailable pronunciation dictionaries, is comparable to phoneme-based recognition for the selected under-resourced languages in both the monolingual and cross-lingual speech recognition tasks. The study significantly demonstrates that context-dependent grapheme-based sub-word units can be reliable for small and medium-large vocabulary speech recognition tasks for these languages. / Telkom SA

The rapid spread of the coronavirus disease (COVID-19) has impacted the lives of millions around the globe. The COVID-19 pandemic has caused increasing concern among treatment professionals about mental health and risky substance use, especially among those who are struggling with a substance use disorder (SUD). The pandemic's impact on those with an SUD may be heightened in vulnerable communities, such as those living in under-resourced and rural areas. Despite policies loosening restrictions on treatment requirements, unintended mental health consequences may arise among this population. We discuss challenges that under-resourced areas face and propose strategies that may improve outcomes for those seeking treatment for SUDs in these areas.

Due to the lack of research on White teacher racial identity development and White graduates of alternative teacher education programs teaching in urban under-resourced schools, this study aimed to: examine how White graduates of alternative teacher education programs perceive race and racism in their urban under-resourced schools, explore the impact of their alternative teacher education programs on their racial identities, and evaluate their abilities to deepen their racial identities in the context of their urban under-resourced schools. Critical examination and analysis of the experiences of White teachers, through the lenses of Critical Race Theory, Critical White Studies, and Howard’s Racial Identity Development Model, provided insights on how quickly expanding alternative teacher education programs across the nation are failing to adequately and critically address White teachers’ racial identity development. Well-intentioned participants recognized a noticeable racial mismatch, did not perceive race or racism in their urban under-resourced schools, lacked exposure to critical coursework, felt unprepared to work with racially dissimilar students, faced difficulties processing their experiences, and showed minimal evidence of having well developed racial identities. Alternative teacher education programs are recommended to prioritize race issues and racial identity development by providing opportunities for White educators to perceive race, adequately preparing and supporting White teachers, and implementing Howard’s (2006) Racial Identity Development Model.

The range of applications for high-quality automatic speech recognition (ASR) systems has grown dramatically with the advent of smart phones, in which speech recognition can greatly enhance the user experience. Currently, the languages with extensive ASR support on these devices are languages that have thousands of hours of transcribed speech corpora already collected. Developing a speech system for such a language is made simpler because extensive resources already exist. However for languages that are not as prominent, the process is more difficult. Many obstacles such as reliability and cost have hampered progress in this regard, and various separate tools for every stage of the development process have been developed to overcome these difficulties. Developing a system that is able to combine these identified partial solutions, involves customising existing tools and developing new ones to interface the overall end-to-end process. This work documents the integration of several tools to enable the end-to-end development of an Automatic Speech Recognition system in a typical under-resourced language. Google App Engine is employed as the core environment for data verification, storage and distribution, and used in conjunction with existing tools for gathering text data and for speech data recording. We analyse the data acquired by each of the tools and develop an ASR system in Shona, an important under-resourced language of Southern Africa. Although unexpected logistical problems complicated the process, we were able to collect a useable Shona speech corpus, and develop the first Automatic Speech Recognition system in that language. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2014

The range of applications for high-quality automatic speech recognition (ASR) systems has grown dramatically with the advent of smart phones, in which speech recognition can greatly enhance the user experience. Currently, the languages with extensive ASR support on these devices are languages that have thousands of hours of transcribed speech corpora already collected. Developing a speech system for such a language is made simpler because extensive resources already exist. However for languages that are not as prominent, the process is more difficult. Many obstacles such as reliability and cost have hampered progress in this regard, and various separate tools for every stage of the development process have been developed to overcome these difficulties. Developing a system that is able to combine these identified partial solutions, involves customising existing tools and developing new ones to interface the overall end-to-end process. This work documents the integration of several tools to enable the end-to-end development of an Automatic Speech Recognition system in a typical under-resourced language. Google App Engine is employed as the core environment for data verification, storage and distribution, and used in conjunction with existing tools for gathering text data and for speech data recording. We analyse the data acquired by each of the tools and develop an ASR system in Shona, an important under-resourced language of Southern Africa. Although unexpected logistical problems complicated the process, we were able to collect a useable Shona speech corpus, and develop the first Automatic Speech Recognition system in that language. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2014

Les langues en Malaisie meurent à un rythme alarmant. A l'heure actuelle, 15 langues sont en danger alors que deux langues se sont éteintes récemment. Une des méthodes pour sauvegarder les langues est de les documenter, mais c'est une tâche fastidieuse lorsque celle-ci est effectuée manuellement.Un système de reconnaissance automatique de la parole (RAP) serait utile pour accélérer le processus de documentation de ressources orales. Cependant, la construction des systèmes de RAP pour une langue cible nécessite une grande quantité de données d'apprentissage comme le suggèrent les techniques actuelles de l'état de l'art, fondées sur des approches empiriques. Par conséquent, il existe de nombreux défis à relever pour construire des systèmes de transcription pour les langues qui possèdent des quantités de données limitées.L'objectif principal de cette thèse est d'étudier les effets de l'utilisation de données de langues étroitement liées, pour construire un système de RAP pour les langues à faibles ressources en Malaisie. Des études antérieures ont montré que les méthodes inter-lingues et multilingues pourraient améliorer les performances des systèmes de RAP à faibles ressources. Dans cette thèse, nous essayons de répondre à plusieurs questions concernant ces approches: comment savons-nous si une langue est utile ou non dans un processus d'apprentissage trans-lingue ? Comment la relation entre la langue source et la langue cible influence les performances de la reconnaissance de la parole ? La simple mise en commun (pooling) des données d'une langue est-elle une approche optimale ?Notre cas d'étude est l'iban, une langue peu dotée de l'île de Bornéo. Nous étudions les effets de l'utilisation des données du malais, une langue locale dominante qui est proche de l'iban, pour développer un système de RAP pour l'iban, sous différentes contraintes de ressources. Nous proposons plusieurs approches pour adapter les données du malais afin obtenir des modèles de prononciation et des modèles acoustiques pour l'iban.Comme la contruction d'un dictionnaire de prononciation à partir de zéro nécessite des ressources humaines importantes, nous avons développé une approche semi-supervisée pour construire rapidement un dictionnaire de prononciation pour l'iban. Celui-ci est fondé sur des techniques d'amorçage, pour améliorer la correspondance entre les données du malais et de l'iban.Pour augmenter la performance des modèles acoustiques à faibles ressources, nous avons exploré deux techniques de modélisation : les modèles de mélanges gaussiens à sous-espaces (SGMM) et les réseaux de neurones profonds (DNN). Nous avons proposé, dans ce cadre, des méthodes de transfert translingue pour la modélisation acoustique permettant de tirer profit d'une grande quantité de langues “proches” de la langue cible d'intérêt. Les résultats montrent que l'utilisation de données du malais est bénéfique pour augmenter les performances des systèmes de RAP de l'iban. Par ailleurs, nous avons également adapté les modèles SGMM et DNN au cas spécifique de la transcription automatique de la parole non native (très présente en Malaisie). Nous avons proposé une approche fine de fusion pour obtenir un SGMM multi-accent optimal. En outre, nous avons développé un modèle DNN spécifique pour la parole accentuée. Les deux approches permettent des améliorations significatives de la précision du système de RAP. De notre étude, nous observons que les modèles SGMM et, de façon plus surprenante, les modèles DNN sont très performants sur des jeux de données d'apprentissage en quantité limités. / Languages in Malaysia are dying in an alarming rate. As of today, 15 languages are in danger while two languages are extinct. One of the methods to save languages is by documenting languages, but it is a tedious task when performed manually.Automatic Speech Recognition (ASR) system could be a tool to help speed up the process of documenting speeches from the native speakers. However, building ASR systems for a target language requires a large amount of training data as current state-of-the-art techniques are based on empirical approach. Hence, there are many challenges in building ASR for languages that have limited data available.The main aim of this thesis is to investigate the effects of using data from closely-related languages to build ASR for low-resource languages in Malaysia. Past studies have shown that cross-lingual and multilingual methods could improve performance of low-resource ASR. In this thesis, we try to answer several questions concerning these approaches: How do we know which language is beneficial for our low-resource language? How does the relationship between source and target languages influence speech recognition performance? Is pooling language data an optimal approach for multilingual strategy?Our case study is Iban, an under-resourced language spoken in Borneo island. We study the effects of using data from Malay, a local dominant language which is close to Iban, for developing Iban ASR under different resource constraints. We have proposed several approaches to adapt Malay data to obtain pronunciation and acoustic models for Iban speech.Building a pronunciation dictionary from scratch is time consuming, as one needs to properly define the sound units of each word in a vocabulary. We developed a semi-supervised approach to quickly build a pronunciation dictionary for Iban. It was based on bootstrapping techniques for improving Malay data to match Iban pronunciations.To increase the performance of low-resource acoustic models we explored two acoustic modelling techniques, the Subspace Gaussian Mixture Models (SGMM) and Deep Neural Networks (DNN). We performed cross-lingual strategies using both frameworks for adapting out-of-language data to Iban speech. Results show that using Malay data is beneficial for increasing the performance of Iban ASR. We also tested SGMM and DNN to improve low-resource non-native ASR. We proposed a fine merging strategy for obtaining an optimal multi-accent SGMM. In addition, we developed an accent-specific DNN using native speech data. After applying both methods, we obtained significant improvements in ASR accuracy. From our study, we observe that using SGMM and DNN for cross-lingual strategy is effective when training data is very limited.