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.

No description available.

Thesis (M.Sc.(Computer Science )) -- University of Limpopo, 2020 / Language modelling (LM) work for under-resourced languages that does not consider most linguistic information inherent in a language produces language models that in adequately represent the language, thereby leading to under-development of natural language processing tools and systems such as speech recognition systems. This study investigated the influence that the orthography (i.e., writing system) of a lan guage has on the quality and/or robustness of the language models created for the text of that language. The unique conjunctive and disjunctive writing systems of isiN debele (Ndebele) and Sepedi (Pedi) were studied. The text data from the LWAZI and NCHLT speech corpora were used to develop lan guage models. The LM techniques that were implemented included: word-based n gram LM, LM smoothing, LM linear interpolation, and higher-order n-gram LM. The toolkits used for development were: HTK LM, SRILM, and CMU-Cam SLM toolkits. From the findings of the study – found on text preparation, data pooling and sizing, higher n-gram models, and interpolation of models – it is concluded that the orthogra phy of the selected languages does have effect on the quality of the language models created for their text. The following recommendations are made as part of LM devel opment for the concerned languages. 1) Special preparation and normalisation of the text data before LM development – paying attention to within sentence text markers and annotation tags that may incorrectly form part of sentences, word sequences, and n-gram contexts. 2) Enable interpolation during training. 3) Develop pentagram and hexagram language models for Pedi texts, and trigrams and quadrigrams for Ndebele texts. 4) Investigate efficient smoothing method for the different languages, especially for different text sizes and different text domains / National Research Foundation (NRF) Telkom University of Limpopo

Syftet med denna fallstudie är att beskriva chefer och anställdas upplevelser av distansarbete efter en återgång till kontoret, genom att koppla till faktorer som organisatoriskt engagemang och produktivitet. Tidigare har forskningen varit inriktad på distansarbetets effekter under Covid-19-pandemin, däremot finns det en brist av forskning inom området efter pandemin. Studien bidrar till kunskap inom området genom att koppla upplevelser av distansarbete till den teoretiska ingången resursbaserat perspektiv. Studiens forskningsmetod är kvalitativ och semistrukturerade intervjuer har genomförts. Den övergripande slutsatsen av studien är att chefer och anställdas upplevelser av distansarbete har resulterat i en positiv inställning till arbetsmodellen. Det som däremot är den främsta orsaken till den positiva inställningen är den hybrida arbetsmodellen som företaget som ingår i studien använt. Chefer och anställda föredrar en kombination av både distans- och kontorsarbete. Detta har även gjort att faktorerna produktivitet och organisatoriskt engagemang har upplevts olika beroende på olika situationer av arbetsmodellerna. Studiens bidrag är därmed att belysa vikten av att kunna kombinera distansarbete och kontorsarbete. / The purpose of this case study is to describe managers’ and employees’ experiences of remote work after returning to the office, by connecting to factors like organizational commitment and productivity. Previous research has been focused on the effects of remote work during the Covid-19 pandemic. However, there is a dearth of research on this topic after the pandemic. This study contributes to knowledge on this area by connecting experiences of remote work to the theoretical model of resource-based perspective. The research method of this study is qualitative and semi-structured interviews have been conducted. The overall conclusion of the study is that managers’ and employees’ experiences of remote work have resulted in a positive attitude to the working model. Thefore most cause of this positive attitude is the hybrid model used by the company analysed in this study. Managers and employees prefer a combination of both remote and office work. This has also led to the factors of productivity and organizational commitment have been experienced differently due to different situations of the work models. The contribution of the study is thus to show the importance of being able to combine remote work and office work.

Dans le cadre des travaux en cours pour informatiser un grand nombre de langues « peu dotées », en particulier celles de l’espace francophone, nous avons créé un système de traduction automatique français-somali dédié à un sous-langage journalistique, permettant d’obtenir des traductions de qualité, à partir d’un corpus bilingue construit par post-édition des résultats de Google Translate (GT), à destination des populations somalophones et non francophones de la Corne de l’Afrique. Pour cela, nous avons constitué le tout premier corpus parallèle français-somali de qualité, comprenant à ce jour 98 912 mots (environ 400 pages standard) et 10 669 segments. Ce dernier constitue’est un corpus aligné, et de très bonne qualité, car nous l’avons construit en post-éditant les pré-traductions de GT, qui combine pour cela avec une combinaison de lason système de TA français-anglais et système de TA anglais-somali. Il Ce corpus a également fait l’objet d’une évaluation de la part depar 9 annotateurs bilingues qui ont donné une note score de qualité à chaque segment du corpus, et corrigé éventuellement notre post-édition. À partir de ce corpus, en croissance, nous avons construit plusieurs versions successives d’un système de Traduction Automatique à base de fragments (PBMT), MosesLIG-fr-so, qui s’est révélé meilleur que GoogleTranslate GT sur ce couple de langues et ce sous-langage, en termes de mesure BLEU et du temps de post-édition. Nous avons fait également une première expérience de traduction automatique neuronale français-somali en utilisant OpenNMT, de façon à améliorer les résultats de la TA sans aboutir à des temps de calcul prohibitifs, tant durant l’entraînement que durant le décodage.D’autre part, nous avons mis en place une iMAG (passerelle interactive d’accès multilingue) qui permet à des internautes somaliens non francophones du continent d’accéder en somali à l’édition en ligne du journal « La Nation de Djibouti ». Les segments (phrases ou titres) prétraduits automatiquement par notre un système de TA fr-so en ligne disponible peuvent être post-édités et notés (sur sur une échelle de 1 à 20) par les lecteurs eux-mêmes, de façon à améliorer le système par apprentissage incrémental, de la même façon que ce qui a été fait pour le système français-chinois (PBMT) créé par [Wang, 2015]. / As part of ongoing work to computerize a large number of "poorly endowed" languages, especially those in the French-speaking world, we have created a French-Somali machine translation system dedicated to a journalistic sub-language, allowing to obtain quality translations from a bilingual body built by post-editing of GoogleTranslate results for the Somali and non-French speaking populations of the Horn of Africa. For this, we have created the very first quality French-Somali parallel corpus, comprising to date 98,912 words (about 400 standard pages) and 10,669 segments. The latter is an aligned corpus of very good quality, because we built in by post-editions editing pre-translations of produced by GT, which uses with a combination of the its French-English and English-Somali MT language pairs. It That corpus was also evaluated by 9 bilingual annotators who gave assigned a quality note score to each segment of the corpus and corrected our post-editing. From Using this growing body corpus as training corpusof work, we have built several successive versions of a MosesLIG-fr-so fragmented statistical Phrase-Based Automatic Machine Translation System (PBMT), which has proven to be better than GoogleTranslate on this language pair and this sub-language, in terms BLEU and of post-editing time. We also did used OpenNMT to build a first French-Somali neural automatic translationMT system and experiment it.in order to improve the results of TA without leading to prohibitive calculation times, both during training and during decoding.On the other hand, we have set up an iMAG (multilingual interactive access gateway) that allows non-French-speaking Somali surfers on the continent to access the online edition of the newspaper "La Nation de Djibouti" in Somali. The segments (sentences or titles), pre- automatically translated automatically by our any available fr-so MT system, can be post-edited and rated (out on a 1 to of 20scale) by the readers themselves, so as to improve the system by incremental learning, in the same way as the has been done before for the French-Chinese PBMT system. (PBMT) created by [Wang, 2015].

Speech recognition in Turkish Language is a challenging problem in several perspectives. Most of the challenges are related to the morphological structure of the language. Since Turkish is an agglutinative language, it is possible to generate many words from a single stem by using suffixes. This characteristic of the language increases the out-of-vocabulary (OOV) words, which degrade the performance of a speech recognizer dramatically. Also, Turkish language allows words to be ordered in a free manner, which makes it difficult to generate robust language models. In this thesis, the existing models and approaches which address the problem of Turkish LVCSR (Large Vocabulary Continuous Speech Recognition) are explored. Different recognition units (words, morphs, stem and endings) are used in generating the n-gram language models. 3-gram and 4-gram language models are generated with respect to the recognition unit. Since the solution domain of speech recognition is involved with machine learning, the performance of the recognizer depends on the sufficiency of the audio data used in acoustic model training. However, it is difficult to obtain rich audio corpora for the Turkish language. In this thesis, existing approaches are used to solve the problem of Turkish LVCSR by using a limited audio corpus. We also proposed several data selection approaches in order to improve the robustness of the acoustic model.

As building transcribed speech corpora for under–resourced languages plays a pivotal role in developing automatic speech recognition (ASR) technologies for such languages, a key step in developing these technologies is the effective collection of ASR data, consisting of transcribed audio and associated meta data. The problem is that no suitable tool currently exists for effectively collecting ASR data for such languages. The specific context and requirements for effectively collecting ASR data for underresourced languages, render all currently known solutions unsuitable for such a task. Such requirements include portability, Internet independence and an open–source code–base. This work documents the development of such a tool, called Woefzela, from the determination of the requirements necessary for effective data collection in this context, to the verification and validation of its functionality. The study demonstrates the effectiveness of using smartphones without any Internet connectivity for ASR data collection for under–resourced languages. It introduces a semireal– time quality control philosophy which increases the amount of usable ASR data collected from speakers. Woefzela was developed for the Android Operating System, and is freely available for use on Android smartphones, with its source code also being made available. A total of more than 790 hours of ASR data for the eleven official languages of South Africa have been successfully collected with Woefzela. As part of this study a benchmark for the performance of a new National Centre for Human Language Technology (NCHLT) English corpus was established. / Thesis (M.Ing. (Electrical Engineering))--North-West University, Potchefstroom Campus, 2012.