59 research outputs found
Query-by-Example Keyword Spotting
Tato diplomová práce se zabývá moderními přístupy detekce klíčových slov a detekce frází v řečových datech. V úvodní části je seznámení s problematikou a teoretický popis metod pro detekci. Následuje popis reprezentace vstupních datových sad použitých při experimentech a evaluaci. Dále jsou uvedeny metody pro detekci klíčových slov definovaných vzorem. Následně jsou popsány evaluační metody a techniky použité pro skórování. Po provedení experimentů na datových sadách a po evaluaci jsou diskutovány výsledky. V dalším kroku jsou navrženy a poté implementovány moderní postupy vedoucí k vylepšení systému pro detekci a opět je provedena evaluace a diskuze dosažených výsledků. V závěrečné části je práce zhodnocena a jsou zde navrženy další směy vývoje našeho systému. Příloha obsahuje manuál pro používání implementovaných skriptů.The aim of the thesis is to get acquainted with modern approach of keyword spotting and spoken term detection in speech data. The bases of keyword spotting are described at first. The data representation used for experiments and evaluation are introduced. Keyword spotting methods where query is provided as an audio example (Query-by-Example) are presented. The scoring metrics are described and experiments follow. The results are discussed. Further, modern approaches of keyword spotting are suggested and implemented. The system with new techniques is evaluated and the discussion of results achieved follows. The conclusions are drawn and the discussion of future directions of development is held. The Appendix contains user manual for using implemented system.
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
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