Voice Versus Keyboard and Mouse for Text Creation on Arabic User Interfaces

The International Arab Journal of Information Technology ◽

10.34028/iajit/19/1/15 ◽

2022 ◽

Author(s):

Khalid Majrashi

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

User Interfaces ◽

Voice Recognition ◽

User Interaction ◽

Learning Curves ◽

User Performance ◽

Voice User

Voice User Interfaces (VUIs) are increasingly popular owing to improvements in automatic speech recognition. However, the understanding of user interaction with VUIs, particularly Arabic VUIs, remains limited. Hence, this research compared user performance, learnability, and satisfaction when using voice and keyboard-and-mouse input modalities for text creation on Arabic user interfaces. A Voice-enabled Email Interface (VEI) and a Traditional Email Interface (TEI) were developed. Forty participants attempted pre-prepared and self-generated message creation tasks using voice on the VEI, and the keyboard-and-mouse modal on the TEI. The results showed that participants were faster (by 1.76 to 2.67 minutes) in pre-prepared message creation using voice than using the keyboard and mouse. Participants were also faster (by 1.72 to 2.49 minutes) in self-generated message creation using voice than using the keyboard and mouse. Although the learning curves were more efficient with the VEI, more participants were satisfied with the TEI. With the VEI, participants reported problems, such as misrecognitions and misspellings, but were satisfied about the visibility of possible executable commands and about the overall accuracy of voice recognition.

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Personification of voice user interfaces: Impacts on user performance

PsycEXTRA Dataset ◽

10.1037/e577582012-011 ◽

2006 ◽

Author(s):

Benjamin A. Knott ◽

Philip Kortum

Keyword(s):

User Interfaces ◽

User Performance ◽

Voice User

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A New Digital Solution Helps Automatic Voice Recognition

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.4.16769 ◽

2018 ◽

Vol 7 (3.4) ◽

pp. 177

Author(s):

M ABOULKHIR ◽

S BOUREKKADI ◽

S KHOULJI ◽

K SLIMANI ◽

M L. KERKEB

Keyword(s):

Information Systems ◽

Speech Recognition ◽

Programming Language ◽

Automatic Speech Recognition ◽

Systems Engineering ◽

Research Group ◽

Voice Recognition ◽

Scientific Work ◽

Home Automation ◽

Engineering Research

This scientific work concerning an examination on automatic speech recognition (ASR) frameworks connected with the home automation and to express the importance of this academic work, an itemized investigation of the engineering of speech recognition frameworks was completed. Our goal in Information Systems Engineering Research Group ofAbdelmalekEssaadi University is to choose a speech recognition programming that must work in remote speech conditions and in a rowdy area.The proposed framework is using atoolbox called Kaldi, which must correspond as aclient created by an advanced programming language, with any home automation framework.

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Integration of an industrial robot with the systems for image and voice recognition

Serbian Journal of Electrical Engineering ◽

10.2298/sjee1301219t ◽

2013 ◽

Vol 10 (1) ◽

pp. 219-230 ◽

Cited By ~ 8

Author(s):

Jovica Tasevski ◽

Milutin Nikolic ◽

Dragisa Miskovic

Keyword(s):

Computer Vision ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Vision System ◽

Industrial Robot ◽

Voice Recognition ◽

Computer Vision System ◽

Human Speech ◽

Position And Orientation ◽

Asr System

The paper reports a solution for the integration of the industrial robot ABB IRB140 with the system for automatic speech recognition (ASR) and the system for computer vision. The robot has the task to manipulate the objects placed randomly on a pad lying on a table, and the computer vision system has to recognize their characteristics (shape, dimension, color, position, and orientation). The ASR system has a task to recognize human speech and use it as a command to the robot, so the robot can manipulate the objects.

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Is human-human spoken interaction manageable? The emergence of the concept: ‘Conversation Intelligence’

Online Journal of Applied Knowledge Management ◽

10.36965/ojakm.2018.6(1)1-14 ◽

2018 ◽

Vol 6 (1) ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Vered Silber-Varod

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

User Interfaces ◽

Power Relations ◽

Large Scale ◽

Human Communication ◽

Communication Management ◽

Mining Technology ◽

Speech Interaction ◽

Spoken Interaction

Currently, via the mediation of audio mining technology and conversational user interfaces, and after years of constant improvements of Automatic Speech Recognition technology, conversation intelligence is an emerging concept, significant to the understanding of human-human communication in its most natural and primitive channel – our voice. This paper introduces the concept of Conversation Intelligence (CI), which is becoming crucial to the study of humanhuman speech interaction and communication management and is part of the field of speech analytics. CI is demonstrated on two established discourse terms – power relations and convergence. Finally, this paper highlights the importance of visualization for large-scale speech analytics.

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Development of End – to – End Encoder - Decoder Model Applying Voice Recognition System in Different Channels

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1267.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 2350-2352

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Error Rate ◽

Voice Recognition ◽

Ground Truth ◽

Recognition System ◽

Training Algorithms ◽

Word Error Rate ◽

End To End ◽

Evaluation Metric

the dissimilarity in recognizing the word sequence and their ground truth in different channels can be absorbed by implementing Automatic Speech Recognition which is the standard evaluation metric and is encountered with the phenomena of Word Error Rate for various measures. In the model of 1ch, the track is trained without any preprocessing and study on multichannel end-to-end Automatic Speech Recognition envisaged that the function can be integrated into (Deep Neural network) – based system and lead to multiple experimental results. More so, when the Word Error Rate (WER) is not directly differentiable, it is pertinent to adopt Encoder – Decoder gradient objective function which has been clear in CHiME-4 system. In this study, we examine that the sequence level evaluation metric is a fair choice for optimizing Encoder – Decoder model for which many training algorithms is designed to reduce sequence level error. The study incorporates the scoring of multiple hypotheses in decoding stage for improving the decoding result to optimum. By this, the mismatch between the objectives is resulted in a feasible form to the maxim. Hence, the study finds the result of voice recognition which is most effective for adaptation.

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