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

Ping, Li; Hua, Zhang; Tsai, Sang‐Bing

doi:10.1155/2021/3018285

Cited by 6 publications

(6 citation statements)

References 26 publications

(29 reference statements)

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“…In Figure 2, L is the size of the training or test set; E/L reflects the performance of training or testing, and experimental method then introduce the training and testing based on probability mean and probability space distance. From the experimental results, the performance of the test results is 16.4% lower than that of the training results, indicating that the stability of the link evaluation algorithm based on the Sugeno integral is relatively good [22].…”

Section: Experimental Results and Analysismentioning

confidence: 88%

Automatic English Pronunciation Evaluation Algorithm Based on Sequence Matching and Feature Fusion

Liu

2022

Mathematical Problems in Engineering

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This article focuses on the question answering type of automatic scoring system for large-scale spoken English examinations and scores using a method called multifeature fusion. Three types of features are extracted to score using speech recognition text as the research object. The three types of features are similarity, syntactic, and phonetic. There are nine distinct characteristics that describe the relationship between examinee responses and expert ratings. Manhattan distance is improved as a measure of similarity in the similarity feature. Simultaneously, a feature of keyword coverage based on editing distance is proposed, and the phenomenon of word variation in text recognition is fully considered, in order to provide examinees with an objective and fair score. To obtain the machine score, all extracted features were fused using a multiple linear regression model. The experimental results demonstrate that the extracted features are extremely effective for machine scoring, with the system scoring performance of the examinee as a unit equaling 98.4 percent of expert scoring performance.

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Section: Experimental Results and Analysismentioning

confidence: 88%

Automatic English Pronunciation Evaluation Algorithm Based on Sequence Matching and Feature Fusion

Liu

2022

Mathematical Problems in Engineering

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“…e flow of the scoring system is shown in Figure 10. Preprepared acoustic modeling and music modeling are used as the answer model using speech recognition technology, and the differences in speech test and models are identified and scored, working with the scoring mechanism [20][21][22][23][24][25][26][27][28].…”

Section: Hmm-based Scoring Methodmentioning

confidence: 99%

An Auxiliary Teaching System for Spoken English Based on Speech Recognition Technology

Bao

2022

Scientific Programming

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Because the English language has always been inaccurate and seemed difficult to correct errors, this development has created a reputation based on improvements to the DWJ algorithm and HMM speech scores and correction mistake. In this paper, different signal characteristics are used using the DWJ algorithm: the Mel frequency cepstrum coefficient compares the standard speech library and the distance between the speech sample and the sample message received. The conversation deciphered the Viterbi code according to the HMM model, which was recognized and evaluated by posteriori probabilities. Finally, the professional data were used to fix the wrong phone to determine, score, and make repair mistakes. The results of the experiments show that the tests used in this article are reliable. The results of the experiment show that the standard English language proficiency in this article is reliable, which can provide students with timely, accurate, and objective assessment and teaching feedback, improving English language proficiency.

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“…A few recent studies related to oral English evaluation system are reviewed below, Ping Li et al [9] developed an automatic scoring approach to evaluate the oral English test. Conventional speech signal analysis focuses on the capture of informative attributes, which degrades the evaluation accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…The output of the DBN is in the form of probability function (scores), which is expressed in Eqn. (9).…”

Section: Adaptive Scoring Algorithmmentioning

confidence: 99%

Design and Development of an Oral English Evaluation System Based on Speech Recognition Technology and A Scoring Algorithm

Haiyan Li

2024

jes

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Proficiency in oral English plays a significant role in different professional and academic settings. However, evaluating oral English proficiency precisely is challenging because of the subjective nature of oral communication. In this study, we developed an innovative Oral English Evaluation System (OEES) by combining the efficiencies of speech recognition technology and scoring algorithms. Initially, an intensive oral English database was collected, containing numerous persons' voice recordings. The collected voice recordings are pre-processed to remove the background noise, ensuring clarity in the voice signals. Then, an automatic speech recognition algorithm was developed using the recurrent neural network (RNN) to transcribe voice signals into text. This module trains the system to map the voice signals to their corresponding textual characteristics. Finally, an adaptive scoring algorithm was incorporated into the designed OEES to evaluate the oral English proficiency of the individuals. The adaptive scoring algorithm considers different factors like fluency, grammar, pronunciation, and vocabulary for evaluating the individual's oral English proficiency as "excellent," "good," and "poor." The presented framework was modeled in Python and validated across diverse natural English-speaking databases. The experimental results are assessed in terms of accuracy, precision, recall, and error rate. The implementation outcomes suggest that the proposed OEES framework accurately evaluates oral English proficiency.

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

Cited by 6 publications

References 26 publications

Automatic English Pronunciation Evaluation Algorithm Based on Sequence Matching and Feature Fusion

Automatic English Pronunciation Evaluation Algorithm Based on Sequence Matching and Feature Fusion

An Auxiliary Teaching System for Spoken English Based on Speech Recognition Technology

Design and Development of an Oral English Evaluation System Based on Speech Recognition Technology and A Scoring Algorithm

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