Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

Probabilistic Modelling of Morphologically Rich Languages

This thesis investigates how the sub-structure of words can be accounted for in probabilistic models of language. Such models play an important role in natural language processing tasks such as translation or speech recognition, but often rely on the simplistic assumption that words are opaque symbols. This assumption does not fit morphologically complex language well, where words can have rich internal structure and sub-word elements are shared across distinct word forms. Our approach is to encode basic notions of morphology into the assumptions of three different types of language models, with the intention that leveraging shared sub-word structure can improve model performance and help overcome data sparsity that arises from morphological processes. In the context of n-gram language modelling, we formulate a new Bayesian model that relies on the decomposition of compound words to attain better smoothing, and we develop a new distributed language model that learns vector representations of morphemes and leverages them to link together morphologically related words. In both cases, we show that accounting for word sub-structure improves the models' intrinsic performance and provides benefits when applied to other tasks, including machine translation. We then shift the focus beyond the modelling of word sequences and consider models that automatically learn what the sub-word elements of a given language are, given an unannotated list of words. We formulate a novel model that can learn discontiguous morphemes in addition to the more conventional contiguous morphemes that most previous models are limited to. This approach is demonstrated on Semitic languages, and we find that modelling discontiguous sub-word structures leads to improvements in the task of segmenting words into their contiguous morphemes.Comment: DPhil thesis, University of Oxford, submitted and accepted 2014. http://ora.ox.ac.uk/objects/uuid:8df7324f-d3b8-47a1-8b0b-3a6feb5f45c

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation. LLMs, because of their large size and pretraining on large volumes of text data, exhibit special abilities which allow them to achieve remarkable performances without any task-specific training in many of the natural language processing tasks. The era of LLMs started with OpenAI GPT-3 model, and the popularity of LLMs is increasing exponentially after the introduction of models like ChatGPT and GPT4. We refer to GPT-3 and its successor OpenAI models, including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With the ever-rising popularity of GLLMs, especially in the research community, there is a strong need for a comprehensive survey which summarizes the recent research progress in multiple dimensions and can guide the research community with insightful future research directions. We start the survey paper with foundation concepts like transformers, transfer learning, self-supervised learning, pretrained language models and large language models. We then present a brief overview of GLLMs and discuss the performances of GLLMs in various downstream tasks, specific domains and multiple languages. We also discuss the data labelling and data augmentation abilities of GLLMs, the robustness of GLLMs, the effectiveness of GLLMs as evaluators, and finally, conclude with multiple insightful future research directions. To summarize, this comprehensive survey paper will serve as a good resource for both academic and industry people to stay updated with the latest research related to GPT-3 family large language models.Comment: Preprint under review, 58 page

Armstrong State College 1990-1991 Catalog

Academic catalog for Armstrong State College

Rexford Guy Tugwell and the New Deal

Thesis (Ph.D.)--Boston UniversityRexford Guy Tugwell, Professor of Economics at Columbia, joined the Roosevelt circle in March, 1932. He was an Assistant Secretary of Agriculture, 1933-34. He helped to write the National Industrial Recovery Act and the Agricultural Adjustment Act. He was an idea man; a publicist ; and an errand boy, bringing academicians, or their ideas, to Roosevelt. He was a member of several inderdepartamental boards. Overestimations of Tugwell's influence rested on the assumption that his intellectual impact on Roosevelt was decisive. Roosevelt used or disregarded Tugwell's ideas as he saw fit. Some policies were in accord with Tugwell's thinking; it is impossible to measure the professor's impact on such matters. Roosevelt took no action on some of Tugwell's ideas, especially those involved in the institutional economist's concept of "conjecture." In one exceptional case, the field of fiscal policy, money, and banking, initial rejection of Tugwell's ideas was followed, to some extent, by thier implementation -- in the "Second" New Deal. Tugwell's impact in this instance was indirect -- he was largely responsible for Marriner S. Eccles' coming to Washington. [TRUNCATED

Winona Daily News

https://openriver.winona.edu/winonadailynews/2032/thumbnail.jp

Bowdoin Orient v.92, no.1-22 (1962-1963)

https://digitalcommons.bowdoin.edu/bowdoinorient-1960s/1003/thumbnail.jp

Albuquerque Morning Journal, 11-07-1909

https://digitalrepository.unm.edu/abq_mj_news/4822/thumbnail.jp