scholarly journals A New Digital Solution Helps Automatic Voice Recognition

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. 

scholarly journals Automatic speech recognition in computer-assisted language learning for individual learning in speaking

2020 ◽  
Vol 5 (2) ◽  
pp. 193-197
Author(s):  
Esti Junining ◽  
Sony Alif ◽  
Nuria Setiarini
Keyword(s):  
Speech Recognition ◽  
Foreign Language ◽  
Language Learning ◽  
Programming Language ◽  
Automatic Speech Recognition ◽  
Computer Assisted ◽  
Computer Assisted Language Learning ◽  
Efl Learners ◽  
C Programming Language ◽  
C Programming

This study is intended to help English as a Foreign Language (EFL) learners in Indonesia to reduce their anxiety level while speaking in front of other people. This study helps to develop an atmosphere that encourages students to practice speaking independently. The interesting atmosphere can be obtained by using Automatic Speech Recognition (ASR) where every student can practice speaking individually without feeling anxious or pressurized, because he/she can practice independently in front of a computer or a gadget. This study used research and development design as it tried to develop a product which can create an atmosphere that encourages students to practice their speaking. The instrument used is a questionnaire which is used to analyze the students’ need of learning English. This study developed a product which utilized ASR technology using C# programming language. This study revealed that the product developed using ASR can make students practice speaking individually without feeling anxious and pressurized.

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

2013 ◽  
Vol 10 (1) ◽  
pp. 219-230 ◽  
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.

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

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.

scholarly journals An HMM-Like Dynamic Time Warping Scheme for Automatic Speech Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ing-Jr Ding ◽  
Yen-Ming Hsu
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Template Matching ◽  
Dynamic Time Warping ◽  
Recognition System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Time Warping ◽  
Feature Based ◽  
Dynamic Time

In the past, the kernel of automatic speech recognition (ASR) is dynamic time warping (DTW), which is feature-based template matching and belongs to the category technique of dynamic programming (DP). Although DTW is an early developed ASR technique, DTW has been popular in lots of applications. DTW is playing an important role for the known Kinect-based gesture recognition application now. This paper proposed an intelligent speech recognition system using an improved DTW approach for multimedia and home automation services. The improved DTW presented in this work, called HMM-like DTW, is essentially a hidden Markov model- (HMM-) like method where the concept of the typical HMM statistical model is brought into the design of DTW. The developed HMM-like DTW method, transforming feature-based DTW recognition into model-based DTW recognition, will be able to behave as the HMM recognition technique and therefore proposed HMM-like DTW with the HMM-like recognition model will have the capability to further perform model adaptation (also known as speaker adaptation). A series of experimental results in home automation-based multimedia access service environments demonstrated the superiority and effectiveness of the developed smart speech recognition system by HMM-like DTW.

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

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.

Toward completely automated vowel extraction: Introducing DARLA

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Sravana Reddy ◽  
James N. Stanford
Keyword(s):  
Information Systems ◽  
Speech Recognition ◽  
Everyday Life ◽  
Automatic Speech Recognition ◽  
Speech Technology ◽  
Closed Captioning ◽  
New Methods ◽  
Vowel Formant

AbstractAutomatic Speech Recognition (ASR) is reaching further and further into everyday life with Apple’s Siri, Google voice search, automated telephone information systems, dictation devices, closed captioning, and other applications. Along with such advances in speech technology, sociolinguists have been considering new methods for alignment and vowel formant extraction, including techniques like the Penn Aligner (

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