Automatic Correction Research Articles

Automatic correction system of english-ukrainian computer translation for technical texts in the field of automation of technological processes

Automatic correction system of english-ukrainian computer translation for technical texts in the field of automation of technological processes

Multi-taper-based automatic correction of non-anechoic antenna measurements

Prototype measurements belong to the key steps in the development of antenna structures. Although accurate validation of their far-field performance can be realized in dedicated facilities, such as anechoic chambers, the high cost of their construction and maintenance might not be justified if the main goal of measurements is to support teaching or low-budget research. Instead, they can be performed in non-anechoic conditions and then refined using appropriate correction algorithms. Unfortunately, the existing post-processing methods suffer from multiple challenges that include manual setup of parameters as well as validation of performance in idealized conditions. In this communication, a multi-taper-based framework for correction of antenna characteristics obtained in non-anechoic environments has been proposed. The algorithm augments one-shot measurements of the structure under test in order to extract the line-of-sight responses while attenuating the interferences pertinent to multi-path propagation and noise from external sources of radiation. The performance of the proposed correction routine has been demonstrated in two test sites using a geometrically small Vivaldi radiator and validated against state-of-the-art techniques from the literature. The uncertainty budget for the measurements performed using the approach amounts to 0.26 dB, which is low given challenging propagation conditions considered for experiments.

A New Interrogation System for FBG Sensors based on Midband Filter with Correction

In this paper, a midband interrogation system for Fiber Bragg Grating (FBG) sensors is presented and its numerical demonstration is reported. The midband interrogation system is based on a finite reflection filter with automatic correction. The filter is used to interrogate the FBG sensors. The strain FBG sensors were used to demonstrate the effectiveness of the proposed systems. The results demonstrate that the proposed system of interrogation has a great resolution (±1 pm) with good stability.

Automatic correction of barometric altimeters using additional air temperature and humidity measurements

A GPS module is often combined with an atmospheric pressure sensor in onboard navigation systems. Thanks to this, using appropriate algorithms comparing the results obtained from GPS and from the barometric altimeter, it is possible to obtain much more accurate indications of the height above sea level. The authors analyzed the problem of correcting barometric altimeter errors caused by non-standard pressure and temperature conditions at sea level and the presence of water vapor in the air. The authors proposed methods of correcting the altimeter error using additional temperature and air humidity measurements. They also proposed a simple method for calibrating the altimeter prior to the mission to estimate the difference in pressure above the standard value. The paper discusses the influence of water vapor on the accuracy of the altimeter indications and the conditions for the applicability of the error correction method. The authors initially considered the possibility of using the method based on the concept of virtual temperature. However, this method was rejected because simulation studies showed that it did not give satisfactory results. Simulation tests were carried out in the MATLAB environment to assess the effectiveness of the proposed methods. The test results turned out to be satisfactory. They can be useful when designing low-cost navigation systems using simple sensors made in MEMS technology. The main envisaged application is in autonomous drone navigation systems.

U-net based vortex detection in Bose–Einstein condensates with automatic correction for manually mislabeled data

Quantum vortices in Bose–Einstein condensates (BECs) are essential phenomena in condensed matter physics, and precisely locating their positions, especially the vortex core, is a precondition for studying their properties. With the rise of machine learning, there is a possibility to expedite the localization process and provide accurate predictions. However, traditional machine learning requires particular considerable amount of manual data annotation, leading to uncontrollable accuracy. In this paper, we utilize the U-Net method to detect vortex positions accurately at the pixel level and propose an Automatic Correction Labeling (ACL) approach to optimize the acquisition of data sets for vortex localization in BECs. This approach addresses inaccuracies in the labeled vortex positions and improves the accuracy of vortex localization, especially the vortex core positions, while enhancing the tolerance for human mislabeling. The main process involves Rough Labeling →\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rightarrow $$\\end{document} Machine Learning →\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rightarrow $$\\end{document} Probability Region Search →\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rightarrow $$\\end{document} Data Relabeling →\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rightarrow $$\\end{document} Machine Learning again. The objective of ACL is to secure more accurate labeled data for model retraining. Through vortex localization experiments conducted in a two-dimensional Bose-Einstein condensate, our results establish the following: 1. Even under conditions of biased and missing manual annotations, U-Net can still accurately locate vortex positions; 2. Vortices exhibit certain regularities, and training U-Net with a small number of samples yields excellent predictive consequences; 3. The machine learning vortex locator based on the ACL method effectively corrects errors in manually annotated data, significantly improving the model’s performance metrics, thus enhancing the precision and metrics of vortex localization. This substantial advancement in the application of machine learning in vortex localization provides an effective way for vortex dynamics localization. Furthermore, this method of obtaining more accurate positions of approximate human labels through machine learning offers new insights for machine learning in other types of image recognition problems.

Safety, Metabolic and Psychological Outcomes of Medtronic MiniMed 780G™ in Children, Adolescents and Young Adults: A Systematic Review.

The MiniMed™ 780G is a second-generation automated insulin delivery system that implements a modified proportional-integral-derivative algorithm with some features of an MD-Logic artificial pancreas algorithm. The system may deliver automatic correction boluses up to every 5min, and it allows the user to choose between three glucose target setpoints (100, 110 and 120mg/dL). We aimed to review the current evidence on this device in children, adolescents, and young adults living with type 1 diabetes. We screened 783 papers, but only 31 manuscripts were included in this review. Data on metabolic outcomes show that this system is safe as regards severe hypoglycaemia and diabetic ketoacidosis. The glycated haemoglobin may drop to levels about 7%, with CGM reports showing a time in range of 75-80%. The time above range and the time below range are within the recommended target in most of the subjects. Few studies evaluated the psychological outcomes. This system seems to be more effective than the first-generation automated insulin delivery systems. The MiniMed™ 780G has been associated with an improvement in sleep quality in subjects living with diabetes and their caregivers, along with an improvement in treatment satisfaction. Psychological distress is as reduced as the glucose control is improved. We also discuss some case reports describing particular situations in clinical practice. Finally, we think that data show that this system is a further step towards the improvement of the treatment of diabetes as concerns both metabolic and psychological outcomes.

Application of Recurrent Neural Networks Based on Attentional Mechanisms in Classification Error Correction in English Teaching

Abstract The way teachers correct errors in English teaching can cause problems such as psychological pressure, and deep learning technology offers the possibility of automatic error correction. In this paper, the final states of left and right texts are computed by constructing two attention mechanisms, target word-independent and related, and the merged obtained vectors are inputted into the RNN model for grammatical error recognition. In collocation error recognition, a Rank-based word candidate set ranking method is added, and error correction for verb usage is semantically encoded using RNN. The study was analyzed and tested in terms of grammar, collocation, and verbs. The ATT-RNN model accuracy is 3.62 percentage points higher than CAMB, and the difference in recall and F 0.5-value is not more than 0.5 percentage points, which indicates that the algorithmic model has some research value.

SOLUTION OF THE SYSTEM OF STATE EQUATIONS OF AN ELECTRICAL NETWORK WITH AN INSULATION DEFECT OR WITH A SINGLE-PHASE GROUND FAULT

A method for solving the system of equations of the state of an electric network with an isolated neutral is proposed, which is created based on the results of measuring the current vectors of the load connection phases and the phase voltages in relation to the ground, which allows to determine the occurrence of insulation defects, including single-phase short circuits to the ground, and also to determine distance to the single-phase ground fault location. In the well-known system of equations of the state of the electrical network, the parameters of the longitudinal resistance of the line phases are additionally taken into account, which consist of the resistance to the shorting point and the resistance beyond the point of shorting the phase to the ground. Due to simplification, the replacement circuit with additional line phase resistances turns into exactly the same circuit as the circuit without taking into account the line phase resistances, due to which the determination of the line resistance to the closing point is performed using a known algorithm. The accuracy of determining the distance to the closing point was evaluated on the mathematical model of the network node. It was established that the main factors affecting accuracy include the ratio of the capacitance values of the line and the network, the distance to the shorting point, as well as the resistance at the shorting point. When using automatic correction, the error in determining the distance to the shorting point with a resistance of 0.1 Ohm does not exceed 0.9% over almost the entire length of the line. Analytical expressions for determining insulation conductivity and the distance to a local insulation defect are proposed. The elements of the algorithm for determining the distance to the location of a local insulation defect and the results of the accuracy assessment of the method for the specific case of a phase-to-ground fault, in which the transient resistance at the fault location is not taken into account, are given. Automatic selection of circuits with low resistance at the point of the circuit is ensured by checking the value of the determined active resistance, as well as checking the voltage of the damaged phase in relation to the ground.

Research on Digital Business English Teaching Mode under the Background of Big Data Technology

Abstract This paper proposes an automatic grammar error correction model based on an encoder-decoder structure, which is combined with a new business English teaching model that can improve the teaching quality of teachers and enable students to get feedback quickly. The model employs a dual encoder structure and extracts source sentence information through an encoder that is based on the attention mechanism. Meanwhile, a fluency-based data augmentation method is also proposed in order to be able to select sentences with different fluency levels based on the fluency ratio between the target sentence and the source sentence. Finally, a Passive-Aggressive algorithm for candidate word selection has been designed, which can avoid generating repetitive and short-biased English sentences. It has been verified that the total grade of the class that incorporates this paper’s model in business English teaching has increased by 9.3 points on average, with the most obvious improvement in business writing by 3 points, followed by translation by 2.8 points. The proposed teaching model in this paper can enhance students’ comprehensive use of business English.

Temperature-dependent photo-elastic coefficient of silicon at 1550 nm

This paper presents a study on the temperature dependent photo-elastic coefficient in single-crystal silicon with (100) and (110) orientations at a wavelength of 1550 nm. The measurement of the photo-elastic coefficient was performed using a polarimetric scheme across a wide temperature range from 5 to 300 K. The experimental setup employed high-sensitivity techniques and incorporated automatic beam path correction, ensuring precise and accurate determination of the coefficient’s values. The results show excellent agreement with previous measurements at room temperature, specifically yielding a value of dn/dsigma = -2.463 times 10^{-11} 1/Pa for the (100) orientation. Interestingly, there is a significant difference in photo-elasticity between the different crystal orientations of approximately 50%. The photo-elastic coefficient’s absolute value increases by approximately 40% with decreasing temperature down to 5 K. These findings provide valuable insights into the photo-elastic properties of silicon and its behavior under varying mechanical stress, particularly relevant for optomechanical precision experiments like cryogenic gravitational wave detectors and microscale optomechanical quantum sensors.

SIGAA: signaling automated analysis: a new tool for Ca2+ signaling quantification using ratiometric Ca2+ dyes

AbstractAstrocytes are non-neural cells, restricted to the brain and spinal cord, whose functions and morphology depend on their location. Astrocyte–astrocyte and astrocyte–neuron interactions occur through cytoplasmic Ca2+ level changes that are assessed to determine cell function and response (i.e., drug testing). The evaluation of alterations in intracellular Ca2+ levels primarily relies on fluorescence imaging techniques, performed through video recording of cells incubated with Ca2+-sensitive dyes. By observing ion concentration shifts over time in a delimited region of interest (ROI) encompassing a single cell, it is possible to draw conclusions on cell responses to specific stimuli. Our work describes a tool named SIGAA—signaling automated analysis, for astrocyte ROI-based fluorescent imaging. This tool is specifically tailored for two wavelengths excited dyes by using two inputs of Ca2+ signaling recorded frames/videos and outputting a set of features relevant to the experiment’s conclusions and cell characterization. SIGAA performs automatic drift correction for the two recorded videos with a template matching algorithm, followed by astrocyte identification (ROI) using morphological reconstruction techniques. Subsequently, SIGAA extracts intracellular Ca2+ evolution functions for all identified ROIs detects function transients, and estimates a set of features for each signal. These features closely resemble those obtained through traditional methods and software used thus far. SIGAA is a new fully automated tool, which can speed up hour-long studies and analysis to a few minutes, showing reliable results as the validity tests indicate.

Development of an augmented Damerau–Levenshtein method for correcting spelling errors in Kazakh texts

The presented paper is devoted to the development of a method for identifying and correcting spelling errors in Kazakh texts. In this paper, the study object is methods for more accurate correction of spelling errors in Kazakh texts. The aim of the study is to develop an augmented version of the Damerau-Levenshtein method for correcting spelling errors in Kazakh language texts. Automatic detection and correction of spelling errors have become a default feature in modern text editors for working with text data, in text messaging applications such as chatbots, messengers, etc. However, although this task is well solved in geographically widespread languages, it has not been fully solved in languages with a small audience, such as the Kazakh language. The methods developed so far cannot correct all spelling errors found in Kazakh texts. Therefore, the development of a method with specific algorithms for spelling error correction in Kazakh texts is considered. As a result of the research work, algorithms for correcting errors found in Kazakh language texts were developed, and the developed algorithms were included in the Damerau-Levenshtein method. The experimental testing results of the augmented Damerau- Levenshtein method showed 97.2 % accuracy in correcting specific errors found only in Kazakh words and 92.8 % accuracy in correcting common errors from letter symbols. The standard Damerau-Levenshtein method testing results showed 76.4 % accuracy in correcting specific errors found only in Kazakh words. The results of the tests in correcting common errors from letter symbols with the standard Damerau-Levenshtein were approximately the same with the augmented Damerau-Levenshtein method, the accuracy is 92.2 %. The extent and conditions of practical application of the results are implemented by including them in text editors, messengers, e-mails and similar applications that work with text data.

A General Relative Radiometric Correction Method for Vignetting Noise Drift

Due to the limitation of the number of sensor pixels, optical splicing is commonly used to improve the imaging width of remote sensing satellites, and this optical stitching can cause vignetting in the image data of adjacent sensors. The weak energy, low signal-to-noise ratio, and poor response stability of vignetting are key factors that restrict the relative radiometric correction of optical splicing remote satellites. This paper proposes a stability analysis method and a relative radiometric correction method for vignetting. First, we analyzed the stability of the response and the noise impact of vignetting. Massive data from the Jilin-1 GF03D satellites was used to analyze the stability of the response using the vignetting stability analysis method. Secondly, the data on the deep sea during nighttime (DDSN) of Jilin-1 GF03D satellites was used to obtain the characteristics of the sensors’ noise. Thirdly, by building a noise drift model, we calculated the coefficient of the noise drift according to its characteristics. Using the coefficient to eliminate the noise drift of each pixel in vignetting can improve the response stability of vignetting. The average response stability increased by 37.64% by this method. Finally, the automatic relative radiometric correction method was completed through histogram matching. Furthermore, we proposed color aberration metrics (CAMs) to evaluate the multi-spectral images after relative radiometric correction, and massive data from the 16 satellites of Jilin-1 GF03D was used to verify the effectiveness and generality. The experimental results show that the average CAM of the images increased by 15.97% using the proposed method compared to the traditional method.

Automatic correction of performance drift under acquisition shift in medical image classification

Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution. We illustrate the effectiveness of the proposed method to detect and correct performance drift in mammography-based breast cancer screening and on publicly available histopathology data. We show that the proposed method can preserve the expected performance in terms of sensitivity/specificity under various realistic scenarios of image acquisition shift, thus offering an important safeguard for clinical deployment.

Filter-Hilbert method for automatic correction of non-anechoic antenna measurements with embedded self-calibration mechanism

One of the most important steps in the process of antenna development involves measurements of its prototype. Far-field performance of radiators is normally characterized in strictly controlled environments such as anechoic chambers which can ensure certification-grade accuracy. Unfortunately, they are also characterized by high construction costs which might not be justified for low-budget research and/or teaching-related activities. Alternatively, the radiation characteristics can be obtained in non-anechoic test sites which neglect the expensive components such as shielding and/or absorbing materials. Although the noise from external radiation sources and multi-path interferences renders direct measurements in such conditions useless for drawing conclusions on antenna performance, the quality of responses can be substantially increased using appropriate post-processing. Unfortunately, the existing techniques are difficult to set-up and prone to failure which makes them of limited use for day-to-day measurements. In this work, a correction framework for non-anechoic measurements based on a set of Hilbert filters automatically adapted to propagation conditions within the test-site has been proposed. The presented method has been validated based on three example antennas and a total of 25 experiments covering 15 unique frequencies of interest. A benchmark of the method against the state-of-the-art correction techniques has also been performed.

A new segment method for pulmonary artery and vein.

Accurate differentiation between pulmonary arteries and veins (A/V) holds pivotal importance in the realm of diagnosing and treating pulmonary ailments. This study presents a new approach that leverages grayscale differences between A/V. Distinctions are measured using median and mean grayscale values within the vessel area. Initially, adherent regions are removed based on vessel structure. The trunk regions are segmented using gray level information near the heart region of the lung boundary. Incorrectly segmented vessels are corrected based on connectivity. For distal lung vessels, a similar distance field is established using a graph-cut method. Experimental results show the algorithm's superior segmentation accuracy, achieving 97.26% compared to the CNN-based average accuracy of 91.67%. Error branches are more concentrated, aiding subsequent manual and automatic correction. This demonstrates the algorithm's effective segmentation of pulmonary A/V.

Arabic spellchecking: a depth-filtered composition metric to achieve fully automatic correction

<p>Digital environments for human learning have evolved a lot in recent years thanks to incredible advances in information technologies. Computer assistance for text creation and editing tools represent a future market in which natural language processing (NLP) concepts will be used. This is particularly the case of the automatic correction of spelling mistakes used daily by data operators. Unfortunately, these spellcheckers are considered writing aids tools, they are unable to perform this task automatically without user’s assistance. In this paper, we suggest a filtered composition metric based on the weighting of two lexical similarity distances in order to reach the auto-correction. The approach developed in this article requires the use of two phases: the first phase of correction involves combining two well-known distances: the edit distance weighted by relative weights of the proximity of the Arabic keyboard and the calligraphical similarity between Arabic alphabet, and combine this measure with the JaroWinkler distance to better weight, filter solutions having the same metric. The second phase is considered as a booster of the first phase, this use the probabilistic bigram language model after the recognition of the solutions of error, which may have the same lexical similarity measure in the first correction phase. The evaluation of the experimental results obtained from the test performed by our filtered composition measure on a dataset of errors allowed us to achieve a 96% of auto-correction rate.</p>

Auto-Correction for Inaccurate Auto-Segmentation of Abdominal MRI by Combining Deep Learning and Active Contour Method

Fast and accurate auto-segmentation is crucial during the magnetic resonance-guided adaptive radiation therapy (MRgART). Deep learning auto-segmentation (DLAS) approaches do not always result in clinically acceptable contours, especially for complex abdominal organs. We have previously reported that the inaccurate DLAS for bowels can be refined using deep learning (DL) and/or active contour method (ACM). This study aims to develop an automatic contour correction (ACC) tool by combining DL and ACM techniques to correct for inaccurate DLAS of pancreas and stomach on MRI. The ACC technique consisted of ACM and DL based on UNet system. The ACM utilizes the probability maps generated from DLAS models to establish 2D parameter maps and to initialize contour evolution, thus not requiring initial parameter adjustments. The Organ specific DL-UNet models were trained for pancreas and stomach contours obtained from a research DLAS tool on abdominal MRIs acquired during routine MRgART from a 1.5T MR-Linac using either turbo field-echo or balanced turbo field-echo sequences. The training dataset contained MR slices along with DLAS and ground truth contours from 54 abdominal MRL sets, and 540 additional augmented sets created by shifting and rotating. DLAS contours were classified based on the expected editing effort into the acceptable, minor edit, or major edit category using an in-house developed classification model. Performance of the obtained ACC models were tested on an independent dataset of 11 sets of abdominal MRIs. For pancreas, the DL-UNet model improved 17% (26/153) and 2% (2/95) of the minor and major edits' slices of the testing dataset, respectively, to acceptable and 39% (37/95) of the major edit slices improved to minor edits. The ACM model improved 3% (4/153) of the minor edit slices to acceptable and the 36% (34/95) of the major slices to minor edits. Using the ACC technique with DL and ACM combined, the percentage of acceptable contours increased from 10% (29/277) to 24% (66/277), and minor edits from 55% (153/277) to 61% (170/277), while the percentage of the major edit slices reduced from 35% (95/277) to 15% (41/277). For stomach, the DL model improved 8% (29/366) of the minor edit slices to acceptable and 50% (16/32) of the major edit slices to minor edit slices. The ACM resulted in 2% (6/366) of minor edit slices to acceptable and 41% (13/32) of major edit slices to minor category. Combining both the DL and ACM, the overall percentage of acceptable stomach contours grew from 13% (58/456) to 22% (101/456) and the percentage of major edit slices reduced from 7% (32/456) to 2% (11/456). The ACC method combining both DL and ACM models can substantially improve the quality of inaccurate DLAS contours of pancreas and stomach in a fully automated and fast manner, minimizing the subsequent manual editing time required for MRgART.

Organ Specific Deep Learning-Based Correction of Inaccurate Auto-Segmentation on Abdominal MRI

Deep learning-based automatic segmentation (DLAS) techniques offer limited success for abdominal organs on MRI, requiring substantial editing time. We have previously developed a deep learning based automatic contour correction (ACC) technique that can correct for inaccurate DLAS contours of bowels on MRI, reducing the manual editing time in MR-guided online adaptive radiation therapy (MRgART). This study aims to develop deep learning-based ACC models for pancreas and duodenum that are particularly difficult to contour either manually or with DLAS. Dense UNet, a deep learning algorithm that combines UNet with dense blocks, was trained to create ACC models. Organ-specific models were trained for pancreas and duodenum contours obtained from a research DLAS tool on MRIs from a 1.5T MR-Linac. The training dataset contained MRI slices paired with DLAS contours from 54 abdominal MRL sets along with ground truth contours and 540 additional augmented sets created by shifting, rotating, and scaling each organ along with the contours and varying the noise and bias field for each patient set. Each DLAS contour was classified into the acceptable (no additional edits required), minor edit (only simple edits required), or major edit category based on the expected editing effort determined using a contour classification model developed in a separate study. The ACC models were trained for the slices requiring minor edit and major edit separately. Performance of the obtained models were tested using an independent 11 MRI sets in term of the change of contour category based on the contour classification model. After applying the duodenum ACC model to the testing datasets, 16% (27/165) and 5% (8/178) of the minor and major edits' slices, respectively, improved to acceptable and 31% (54/178) of the major edit slices improved to minor edits. Furthermore, the total percentage of acceptable contours grew from 10% (36/378) to 19% (71/378) and the percentage of the major edit slices reduced from 47% (178/378) to 30% (115/378). After applying the pancreas ACC model to the testing datasets, 32% (47/143) and 1% (1/96) of the minor and major edits' slices, respectively, improved to acceptable and 49% (47/96) of the major edit slices improved to minor edit slices. Furthermore, the total percentage of acceptable contours grew from 14% (38/277) to 31% (86/277) and the percentage of major edit slices reduced from 35% (96/277) to 17% (48/277). Deep learning based automatic contour corrections can substantially improve inaccurate DLAS contours of pancreas and duodenum on MRI that would otherwise require time-consuming edits, resulting in less manual intervention and increased efficiency during MRgART.

A study on teaching English in higher education based on an improved deep belief network

Abstract This paper combines machine learning with acoustic features to design an automatic pronunciation error correction system. The article first adopts Meier’s inverse spectral coefficients and random forest algorithm to classify and detect learners’ pronunciation errors and clarify learners’ pronunciation problems, from which the MFCC-RF model is proposed. Then, using the feature self-learning capability of deep belief networks and the OneClass idea of SVM, we proposed a DBN-SVM model to overcome the shortcomings of the MFCC-RF model in pronunciation classification and error detection due to unbalanced samples and missing data, which resulted in low error detection rate and poor coverage of error types. By comparing the model’s performance for pronunciation error detection, the DBN-SVM model was more accurate than the other two algorithms in detecting the three error types with a stable accuracy of around 80%. Finally, when the experimental class was taught with the automatic pronunciation error correction system, the experimental class improved by 19.5 points after one semester of study, while the control class only improved by 6.8 points. Hence, the DBN-SVM model-based pronunciation mistake correction system has significantly impacted the speed of change and advancement in English teaching techniques while substantially enhancing the quality of oral pronunciation and learning efficiency of English learners.