Design of Automatic Scoring System for Oral English Test Based on Sequence Matching and Big Data Analysis

With the application of an automatic scoring system to all kinds of oral English tests at all levels, the efficiency of test implementation has been greatly improved. The traditional speech signal processing method only focuses on the extraction of scoring features, which could not ensure the accuracy of the scoring algorithm. Aiming at the reliability of the automatic scoring system, based on the principle of sequence matching, this paper adopts the spoken speech feature extraction method to extract the features of spoken English test pronunciation and establishes a dynamic optimized spoken English pronunciation signal model based on sequence matching, which could maintain good dynamic selection and clustering ability in a strong interference environment. According to the comprehensive experiment, the automatic scoring result of the system is much higher than that of the traditional method, which greatly improves the recognition ability of oral pronunciation, solves the difference between the automatic scoring of the system and the manual scoring, and promotes the computer automatic scoring system to replace or partially replace the manual marking.

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Research on the Continuous Speech Feature Extraction Method for Different Noise

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.3589 ◽

2014 ◽

Vol 513-517 ◽

pp. 3589-3592

Author(s):

Wei Liu ◽

Yi Ming Sun ◽

Yan Xiu Liu

Keyword(s):

Recognition Rate ◽

Noisy Environment ◽

Continuous Speech ◽

Continuous Speech Recognition ◽

Feature Extraction Method ◽

Average Recognition Rate ◽

Speech Feature ◽

The Difference ◽

Noise Robust ◽

Speech Feature Extraction

We propose a noise-robust continuous speech recognition (CSR) method for modeling and recognition. In recognition, we divide the continuous speech vectors to segments using proposed algorithm, then use DRA based on the segments for recognition. The proposed method efficiency is studied for noisy environment. DRA decreases the difference between the model and recognition continuous speech vectors. The new algorithm focuses on adjust the vectors by using different maxima in different segments. Segment-based DRA algorithm can make noisy speech feature vectors closer to the model. The average recognition rate has been improved at different noise and SNR conditions.

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The Difference in Sensing Properties Towards Anions between Two Pyridinium Amide-Based Receptors with a Subtle Change of H-Bond Position

Letters in Organic Chemistry ◽

10.2174/1570180816666190801100603 ◽

2020 ◽

Vol 17 (6) ◽

pp. 472-478

Author(s):

Wei-tao Gong ◽

Wei-dong Qu ◽

Guiling Ning

Keyword(s):

Fluorescence Enhancement ◽

Small Difference ◽

Fluorescence Emission ◽

Subtle Change ◽

Sensing Properties ◽

Naked Eye ◽

Recognition Ability ◽

The Difference ◽

L1 And L2

Two pyridinium amide-based receptors L1 and L2 with a small difference of H-bond position of the amide have been synthesized and characterized. Interestingly, they exhibited a huge difference in sensing towards AcO- and H2PO4 -, respectively. Receptor L1 was found to be ‘naked-eye’ selective for AcO- anions, while receptor L2 showed clear fluorescence enhancement selective to H2PO4 - anion. The recognition ability has been established by fluorescence emission, UV-vis spectra, and 1HNMR titration.

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Automatic Scoring System for CET-4 Compositions Based on Seq2Seq+Bi-LSTM Model

2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) ◽

10.1109/auteee50969.2020.9315678 ◽

2020 ◽

Author(s):

Jun Zhao ◽

Jiulong Chen

Keyword(s):

Scoring System ◽

Automatic Scoring

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RFID-Based Automatic Scoring System for Physical Fitness Testing

IEEE Systems Journal ◽

10.1109/jsyst.2013.2279570 ◽

2015 ◽

Vol 9 (2) ◽

pp. 326-334 ◽

Cited By ~ 3

Author(s):

Sekyoung Youm ◽

Yongwoong Jeon ◽

Seung-Hun Park ◽

Weimo Zhu

Keyword(s):

Physical Fitness ◽

Scoring System ◽

Fitness Testing ◽

Physical Fitness Testing ◽

Automatic Scoring

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Incorporating Protein Topology Information in Similarity Matrices for Improved Sequence Matching (A Fold-Specific Scoring System)

Biophysical Journal ◽

10.1016/j.bpj.2012.11.2804 ◽

2013 ◽

Vol 104 (2) ◽

pp. 508a

Author(s):

Sumudu P. Leelananda ◽

Robert L. Jernigan

Keyword(s):

Scoring System ◽

Sequence Matching ◽

Protein Topology ◽

Topology Information ◽

Similarity Matrices

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Algorithm of Color Detection for Moving Video Objects Based on Mode Matching

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.1000 ◽

2012 ◽

Vol 239-240 ◽

pp. 1000-1003

Author(s):

Zhao Quan Cai ◽

Hui Hu ◽

Tao Xu ◽

Wei Luo ◽

Yi Cheng He

Keyword(s):

Background Subtraction ◽

Moving Objects ◽

Color Matching ◽

Human Beings ◽

Current Image ◽

Color Recognition ◽

Recognition Ability ◽

Color Identification ◽

The Difference ◽

Background Image

It is urgent to study how to effectively identify color of moving objects from the video in the information era. In this paper, we present the color identification methods for moving objects on fixed camera. One kind of the methods is background subtraction that recognizes the foreground objects by compare the difference of pixel luminance between the current image and the background image at the same coordinates. Another kind is based on the statistics of HSV color and color matching which makes the detection more similar to the color identification of the human beings. According to the experiment results, after the completion of the background modelling, our algorithm of background subtraction, statistics of the HSV color and the color matching have strong color recognition ability on the moving objects of video.

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The Role of CO-RADS Scoring System in the Diagnosis of COVID-19 Infection and its Correlation with Clinical Signs

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666210827150937 ◽

2021 ◽

Vol 17 ◽

Author(s):

Şenol Çomoğlu ◽

Sinan Öztürk ◽

Ahmet Topçu ◽

Fatma Kulalı ◽

Aydın Kant ◽

...

Keyword(s):

Scoring System ◽

Clinical Signs ◽

Assessment System ◽

Symptom Duration ◽

Shortness Of Breath ◽

Laboratory Findings ◽

Ct Findings ◽

Chronic Pulmonary Diseases ◽

Ct Evaluation ◽

The Difference

Background: Computed tomography (CT) evaluation systematics has become necessary to eliminate the difference of opinion among radiologists in evaluating COVID-19 CT findings. Introduction: The objectives of this study were to evaluate the efficiency of CO-RADS scoring system in our patients with COVID-19 as well as to examine its correlation with clinical and laboratory findings. Method: The CO-RADS category of all patients included in the study was determined by a radiologist who did not know the rtRT-PCR test result of the patients, according to the Covid-19 reporting and data system of Mathias Prokop et al. Results: A total of 1338 patients were included. CT findings were positive in 66.3%, with a mean CO-RADS score of 3,4 ± 1,7. 444 (33.1%) of the patients were in the CO-RADS 1-2, 894 (66.9%) were in the CO-RADS 3-5 group. There were positive correlations between CO-RADS score and age, CMI, hypertension, diabetes mellitus, chronic pulmonary diseases presence of symptoms, symptom duration, presence of cough, shortness of breath, malaise, CRP, and LDH, while CO-RADS score was negatively correlated with lymphocyte count. The results of the ROC analysis suggested that those with age ≥40 years, symptom duration >2 days, CMI score >1 and/or comorbid conditions were more likely to have a CO-RADS score of 3-5. Conclusion: The CO-RADS classification system is a CT findings assessment system that can be used to diagnose COVID-19 in patients with symptoms of cough, shortness of breath, myalgia and fatigue for more than two days.

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