Select a location, and then pick an alignment style. When I entered my third heading, the styles chart provided a 4th which I used for the next.On the Insert tab, click the Page Number icon, and then click Page Number. Here are the top hidden tips and tricks for Microsoft Word for Mac 2011.Chapter 10 Enhanced Speech-Enabled Tools for Intelligent and Mobile E-Learning ApplicationsTable of Contents (Word for Mac) I have a document with 7 different heading. On the References Ribbon, in the Table of Contents Group, click on the arrow next to the Table of. 0 Comments Place your cursor where you want your table of contents to be. Microsoft Word For Mac Guide On How To Show 4th And 5th Tires On A Table Of Contents.A Web-based learning tool, the Learn IN Context (LINC+) system, designed and used in a real mixed-mode learning context for a computer (C++ language) programming course taught at the Université de Moncton (Canada) is described here. This chapter presents systems that use speech technology to emulate the one-on-one interaction a student can get from a virtual instructor. New Python Flask tutorial - Use VS Code to create and debug Python Flask.Web-based learning is rapidly becoming the preferred way to quickly, efficiently, and economically create and deliver training or educational content through various communication media. Select OK twice to close both dialog boxes.Institut National de Recherche Scientifique-Énergie-Matériaux-Télécommunications, CanadaPreview: Settings editor - Now with a Table of Contents to organize settings. To change the numbering style, select Format and then choose the formatting you want to use.The findings show that when the learning material is delivered in the form ofa collaborative and voice-enabled presentation,the majority oflearnersseem tobesatisfied with this new media, and confirm that it does not negatively affect their cognitive load.In the context of rapidly growing network applications, an abiding vision consists of providing computer-based media support where no sophisticated training is required. The portability of the e-learning system across a mobile platform is also investigated. New Automated Service Agents based on the Artificial Intelligence Markup Language (AIML) are used to provide naturalness to the dialogs between users and machines.This portability has to be investigated in terms of benefits or disadvantages for the learning process. M-learning refers to the use ofmobileandhandheldITdevices,suchascellular telephones, Personal Digital Assistants (PDAs), and laptops, in teaching and learning.As computers and the internet become essential educational tools, the portability of e-learning content across technologies and platforms becomes a critical issue. Recently mobile learning (m-learning) has emerged as a promising means to reach more prospective learners. Numerous studies, including those of Najjar (1996) andAlty (2002), confirm that the type of computer-based media incorporated in e-learning materials can have a significant impact on the amount of information retained, understood, and recalled by learners. In fact, easier-to-use development tools, lower costs, availability of broadband channels and potentially higher returns, in the form of better learner productivity, have made e- learning technology attractive to a wider variety of institutional and individual users.
Microsoft Word Guide On Show 4Th And 5Th Sections On A Table Of Contents How To Show 4ThMoreover, to design an effective e-Learning tool for CL, we must avoidSome common issues arising from applying or developing them. This justifies the development of the proposed “in context” system described below. To some extent, these features switch the system to an interactive and communication system, which may be separated from the underlying learning context. These systems may integrate a form of chat window or forum through a public or private communication channel. An overview on the relevance of including multimedia files into an e-learningEnvironment is given in section 3. Section 2 is concerned with both TTS and ASR background. In this context, an instructor meets with students in the classroom, and a resource base of content material is made available to students through the web.This chapter is further organized as follows. Text analysis aims to analyze the input text. Finally, in Section 6 we conclude and discuss future perspectives of this work.The general architecture of a TTS system has three components: text/linguistic analysis, prosodic generation and synthetic speech generation. Section 5 presents the results of experiments carried out to evaluate how learners deal with the LINC (voiceless) platform, and reports objective and subjective evaluations of the ASR and TTS modules of LINC+. This section also describes the spoken query system for navigatingandsearching,theAIML-basedmethod to enhance the dialog quality, the mobile platform we used to provide more accessibility options to learners,andtheuserprofileandautomatictraining system. These parameters are closely related to speech style, where variations in intonation, pause rhythm, stress and accent, are observed. Prosodic features include pitch contours, energy contours, and duration. The linguistic features are used by the prosodic generator in order to obtain as much naturalness as possible with synthetic speech. At this first level, linguistic information is also extracted through the use of morphologic and syntactic analyzers. Download cheat bakery story mod apkThese units are preferred to phonemic units since they are longer than phonemes. Units such as diphone, syllable, triphone and polyphone are usually adopted by TTS systems. The naturalness and intelligibility of synthetic speech depend strongly on the selected speech units. In general, ASR can be viewed as successive transformations of the acoustic micro-structure of the speech signal into its implicit phonetic macro-structure. Advances in both computing devices and algorithm development have facilitated these historical changes. New TTS-based architectures allow developers to create natural language dialogue systems that combine TTS with natural language speech recognition.Speech recognition has also made enormous progress over the past 20 years. They range from talking document browsers, to personalcomputer-basedagents,tovoice-mailand unified messaging systems, and to new telephone directory services. However, during synthesis, when such units are concatenated back together, it is important to reduce the spectral discontinuity and distortion.Applications using a TTS module are numerous and quite diversified. Modern configurations for ASR are mostly software architectures that generate a sequence of word hypotheses validated by a language model from an acoustic signal.The most popular and effective algorithm implemented in these architectures is based on Hidden MarkovModels (HMMs), which belong to the class of statistical methods (Jelinek, 1997). Thus, according to Deng (2004), it is necessary to unify acoustic processingandtoadaptthearchitectureoftheASR system to cover the broadest range of languages and situations. One of the interesting challenges that ASR faces is building language-independent systems. To reach this goal, it is necessary to suitably describe the phonetic macro-structure, which is usually hidden behind the general knowledge of phonetic science, as studied by Allen (1994) and O’Shaughnessy (2001).
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