Integrating speech enabled services in a Web-based e-learning environment

Wolff, Matthias; Eichner, Matthias; Hoffmann, Rüdiger

doi:10.1109/itcc.2004.1286651

Cited by 4 publications

(7 citation statements)

References 7 publications

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“…This system is used to browse the worldwide web via speech input. Speech based e-learning is described in [10]. In [10], the speech recognition system uses client/server architecture.…”

Section: Literature Surveymentioning

confidence: 99%

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Real Time Speech Based Integrated Development Environment for C Program

Bhagavathsingh¹,

Srinivasan²,

Natrajan³

2016

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This Automatic Speech Recognition (ASR) is the process which converts an acoustic signal captured by the microphone to written text. The motivation of the paper is to create a speech based Integrated Development Environment (IDE) for C program. This paper proposes a technique to facilitate the visually impaired people or the person with arm injuries with excellent programming skills that can code the C program through voice input. The proposed system accepts the C program as voice input and produces compiled C program as output. The user should utter each line of the C program through voice input. First the voice input is recognized as text. The recognized text will be converted into C program by using syntactic constructs of the C language. After conversion, C program will be fetched as input to the IDE. Furthermore, the IDE commands like open, save, close, compile, run are also given through voice input only. If any error occurs during the compilation process, the error is corrected through voice input only. The errors can be corrected by specifying the line number through voice input. Performance of the speech recognition system is analyzed by varying the vocabulary size as well as number of mixture components in HMM.

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“…This system is used to browse the worldwide web via speech input. Speech based e-learning is described in [10]. In [10], the speech recognition system uses client/server architecture.…”

Section: Literature Surveymentioning

confidence: 99%

“…Speech based e-learning is described in [10]. In [10], the speech recognition system uses client/server architecture. The client uses a Java applet, which is integrated in an HTML page.…”

Section: Literature Surveymentioning

confidence: 99%

Real Time Speech Based Integrated Development Environment for C Program

Bhagavathsingh¹,

Srinivasan²,

Natrajan³

2016

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“…3) Termination (16) and denote the neutral elements of the and operators, respectively. Usually, we replace by in (16).…”

Section: ) Induction For A) and B)mentioning

confidence: 99%

“…Usually, we replace by in (16). If we replace by , (16a) is called the Viterbi algorithm [25], [26].…”

Section: ) Induction For A) and B)mentioning

confidence: 99%

“…is used as weight of the feature vector for the estimation of the Gaussian (which is uniquely associated with the transition). Now, we use the reestimated parameters , and and do (16) again to determine the forward and backward probabilities and for all feature vector sequences of the training set. From these we calculate new and according to (17) and (18) and perform another reestimation of the HMM parameters.…”

Section: ) Induction For A) and B)mentioning

confidence: 99%

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Statistical Classifiers for Structural Health Monitoring

Tschöpe

Wolff

2009

IEEE Sensors J.

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Multisensor problems are important tasks in the field of structural health monitoring. By means of signals originating from different sensors, we have to make a decision about the test object. We describe a universal, largely problem independent method, which applies statistical classifiers in order to identify objects or assess their state. This work presents the results of a series of studies, which systematically investigated such approaches for a great variety of technical and biological signals. We give an overview on the theoretical background and describe two selected application examples.

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The Effect of Voice Instruction on the Construction of Mental Model

Restyandito

Chan

Proboyekti

2014

Human-Computer Interaction. Advanced Interaction Modalities and Techniques

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The goal of this study is to observe the effect of instruction deliverance method in the construction of mental model. A good mental model can help the user's learnability process. There were two methods tested in this study: a step-by-step instruction (SS) and a complete set of whole-steps instructions (WS) to finish a given task. The SS group performed better on the learning process, however they had the least score on both the information retention and transfer process. Their minds were not engaged in the process, as they seemed to simply follow the instructions without being critical. When error occurred, they tended to be less persistent in trying to finish the task. This might be caused by the incomplete mental model as a result of receiving the instruction step by step.

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Integrating speech enabled services in a Web-based e-learning environment

Cited by 4 publications

References 7 publications

Real Time Speech Based Integrated Development Environment for C Program

Real Time Speech Based Integrated Development Environment for C Program

Statistical Classifiers for Structural Health Monitoring

The Effect of Voice Instruction on the Construction of Mental Model

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