scholarly journals English Pronunciation Standards Based on Multimodal Acoustic Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lingyi Zhu
Keyword(s):  
Feature Extraction ◽  
Language Learning ◽  
Error Detection ◽  
Video Frame ◽  
Detection Accuracy ◽  
Spoken English ◽  
Error Detection And Correction ◽  
English Pronunciation ◽  
Opening And Closing ◽  
Gradient Enhancement

In recent years, economic globalization is the trend, and communication between countries is getting closer and closer; more and more people begin to pay attention to learning spoken English. The development of computer-aided language learning makes it more convenient for people to learn spoken English; however, the detection and correction of incorrect English pronunciation, which is its core, are still inadequate. In this paper, we propose a multimodal end-to-end English pronunciation error detection and correction model based on audio and video, which does not require phoneme forced alignment of the English pronunciation video signal to be processed, and uses rich audio and video features for English pronunciation error detection, which improves the error detection accuracy to a great extent especially in noisy environments. To address the shortcomings of the current lip feature extraction algorithm which is too complicated and not enough characterization ability, a feature extraction scheme based on the lip opening and closing angle is proposed. The lip syllable frames are obtained by video frame splitting, the syllables are denoised, the key point information of the lips is obtained using a gradient enhancement-based regression tree algorithm, the effects of speaker tilt and movement are removed by scale normalization, and finally, the lip opening and closing angles are calculated using mathematical geometry, and the lip feature values are generated by combining the angle changes.

The Effect of Using Flipped Classroom Model to Improve Iraqi EFL Learners’ Pronunciation in English at University Level

2020 ◽  
Vol 1 (40) ◽  
pp. 631-654
Author(s):  
Khaldoon Waleed Husam Al-Mofti
Keyword(s):  
Language Learning ◽  
Flipped Classroom ◽  
Efl Learners ◽  
English Pronunciation ◽  
Before And After ◽  
Quasi Experimental ◽  
University Level ◽  
The University ◽  
And Control ◽  
Experimental Group

For Iraqi EFL learners who are studying English pronunciation in a traditional instruction method often requires more effort and hard work. Thus, using new methods of teaching such as the flipped classroom model (FCM) is necessary to facilitate learning and improve performance. Hence, this study reports on explanatory research that investigates the effect of using the FCM in the teaching of English pronunciation for Iraqi EFL learners at the university level. The study implemented mixed research methods for data collection in a quasi-experimental analysis. Therefore, two tests were conducted on the assigned groups to measure the effect of the FCM before and after the intervention. Besides, a questionnaire and interviews were used on the experiment group students to collect data about their perceptions of the FCM. The study length (lasted)  was 15 weeks and is comprised of 60 students from the department of English, College of Arts at the University of Anbar. The students were divided into two groups, experimental, and control with 30 students in each group. The findings revealed that there was a significant statistical difference between the two groups in favour of the experimental group with better performance, indicating that the FCM has considerably assisted the Iraqi EFL learners to improve their English pronunciation. Moreover, the students expressed their positive feedback and satisfaction on the use of the FCM in their responses to the questionnaire and the interviews. As such, the current study recommends further research to study the effect of applying the FCM in areas and disciplines other than language learning.

scholarly journals A Tip–Tilt and Piston Detection Approach for Segmented Telescopes

Photonics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 3
Author(s):  
Shun Qin ◽  
Wai Kin Chan
Keyword(s):  
Error Detection ◽  
Phase Retrieval ◽  
Dynamic Range ◽  
Detection Accuracy ◽  
Wavefront Sensing ◽  
Next Generation ◽  
Large Aperture ◽  
Detection Approach ◽  
Segmented Mirror ◽  
And Control

Accurate segmented mirror wavefront sensing and control is essential for next-generation large aperture telescope system design. In this paper, a direct tip–tilt and piston error detection technique based on model-based phase retrieval with multiple defocused images is proposed for segmented mirror wavefront sensing. In our technique, the tip–tilt and piston error are represented by a basis consisting of three basic plane functions with respect to the x, y, and z axis so that they can be parameterized by the coefficients of these bases; the coefficients then are solved by a non-linear optimization method with the defocus multi-images. Simulation results show that the proposed technique is capable of measuring high dynamic range wavefront error reaching 7λ, while resulting in high detection accuracy. The algorithm is demonstrated as robust to noise by introducing phase parameterization. In comparison, the proposed tip–tilt and piston error detection approach is much easier to implement than many existing methods, which usually introduce extra sensors and devices, as it is a technique based on multiple images. These characteristics make it promising for the application of wavefront sensing and control in next-generation large aperture telescopes.

scholarly journals Utilising Flow Aggregation to Classify Benign Imitating Attacks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1761
Author(s):  
Hanan Hindy ◽  
Robert Atkinson ◽  
Christos Tachtatzis ◽  
Ethan Bayne ◽  
Miroslav Bures ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Network Traffic ◽  
Cyber Attacks ◽  
Detection Accuracy ◽  
Computational Power ◽  
Human Understanding ◽  
Flow Aggregation ◽  
Additional Level ◽  
Attack Surfaces

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

scholarly journals Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhang ◽  
Xiaolong Zheng ◽  
Zhanyong Tang ◽  
Tianzhang Xing ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mobile Sensing ◽  
Experimental Results ◽  
Smart Devices ◽  
Detection Accuracy ◽  
Phase Information ◽  
Current Sensing ◽  
Privacy Leakage ◽  
Sensing Systems ◽  
Additional Hardware

Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

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