# An English Teaching Pronunciation Detection and Recognition Algorithm Based on Cluster Analysis and Improved SSD

Authors: Dongxiao Peng
Journal: Journal of Electrical and Computer Engineering
 (2022)

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## Abstract

The accuracy of English pronunciation is the key index to evaluate the quality of English teaching. Correct pronunciation and smooth language flow are the expectations of every student for English learning. Aiming at the poor effect and slow speed of the original SSD (Single Shot MultiBox Detector) algorithm in English teaching pronunciation detection, this paper proposes a clustering and improved SSD algorithm for English teaching pronunciation detection and recognition. The algorithm improves the concept module to enhance the feature extraction ability of the network and improve the detection speed. Meanwhile, it integrates multiscale features to realize multilayer multiplexing and equalization of features, so as to improve the detection effect of small target sound. This algorithm extracts more features by introducing channel attention mechanism, which increases the detection accuracy while reducing computation. In order to optimize the network’s ability to extract target location information, K-means clustering method is used to set the default parameters that are more in line with the characteristics of target samples. The experimental results showed that the proposed algorithm can accurately evaluate the pronunciation quality of reading aloud, so as to effectively reflect the oral English level of the reader.


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## Body

## 1. Introduction

With the deepening of world economic integration and China’s opening to the outside world, English has become an important tool for foreign exchange. Oral English learning and dialogue and communication are indispensable links in the whole process of English learning [1]. There are great differences between Chinese pronunciation and English pronunciation. Chinese people are usually used to using Chinese pronunciation for English pronunciation. This often leads to inaccurate English pronunciation, making it difficult for the other party to understand. Therefore, using accurate English pronunciation is the key to improving the quality of oral English pronunciation [2].Nowadays, computer-assisted English learning has become a trend [3]. As early as 2007, the Ministry of education has clearly put forward the computer-aided English teaching model. At present, most computer-aided language learning systems focus on word memory, grammar learning, and text reading, and are less applied to oral learning [4] because it is very difficult for a computer to judge the quality of speech from the received oral pronunciation. In recent years, speech recognition technology has been applied to computer-aided language learning system [5]. The oral English learning mode of collecting the oral voice of the tester, evaluating and correcting the voice quality in real time has become increasingly popular [6]. For the time being, oral English pronunciation quality evaluation has become the core technology module of computer-aided language learning system. In addition, pronunciation quality evaluation can also be used for the automatic evaluation of oral English computer test, because it cannot be disturbed by subjective factors. Compared with manual judgment, it is more objective and fair, and it can greatly improve efficiency.Aiming at the poor effect and slow speed of the original SSD algorithm in English teaching pronunciation detection, this paper proposes a clustering and improved SSD algorithm for English teaching pronunciation detection and recognition. Based on SSD algorithm, this algorithm designs an improved conception module to replace the new feature extraction layer in the network. K-means clustering method is used to set the default parameters that are more in line with the characteristics of the target samples, so as to optimize the feature extraction ability of the network.This paper mainly has the following innovations:(1)
This technology is helpful to correct learners’ pronunciation errors(2)
It can also help learners better understand and improve their pronunciation level(3)
It is more helpful to foreign language learners who lack language environment and professional guidanceThis paper consists of five main parts: the first part is the introduction, the second part is related work, the third part is the algorithm design, the fourth part is the experiment and analysis, and the fifth part is the conclusion, besides there are abstracts and references.


## 2. Related Work

At present, a lot of research work has been carried out in computer-aided English pronunciation quality evaluation at home and abroad. The evaluation of oral English pronunciation in foreign countries started early, which can be traced back to the early 1990s, and the research in this field has not been stopped. In the 1990s, a voice interactive language training system was proposed [7]. The speech recognition model based on Hidden Markov model scores the speech automatically from the aspects of speech film accuracy, speech film cycle, and speech speed. In order to help learners have interactive oral English dialogue, Cambridge University and MIT are the institutions that carried out the research on oral English pronunciation evaluation earlier. They have jointly carried out a project called oral communication language learning and achieved good research results [8]. Stanford University is another institution that carried out the research on pronunciation quality evaluation earlier, aiming at real-time evaluation of pronunciation quality through interactive oral dialogue. Its representative research results have been applied to computer-aided language learning software such as edu speak and web grader [9]. Similar systems include technology-based assessment of language and literature. The system aims at automatic speech real-time evaluation with words as chunks [10]. Versant is an automatic oral evaluation system developed by American Orient Company. The system has strong function and can locate words and pauses in sentences. It extracts evaluation index data according to language features, and then estimates the evaluation score through multiple linear regression model [11]. In addition, the interactive spoke language education system is mainly aimed at the English pronunciation practice system developed by nonnative English learners, especially those with German background. Due to the built-in evaluation module of pronunciation accuracy, it has the function of detecting the position and type of wrong speech [12].Domestic research on oral English pronunciation evaluation has achieved some successful results [13]. The automatic discrimination system of English reading in the University of Science and technology of China conducts a comprehensive examination of the tester’s fluency, speaking speed, and pronunciation accuracy. Tsinghua University has constructed an automatic evaluation model of English follow-up based on the stress and intonation of the tester [14]. The Chinese Academy of Sciences also used indicators such as accuracy and fluency to evaluate the oral evaluation of second language acquisition. Literature [15] added prosodic indicators to the original pronunciation quality evaluation system and constructed a prosodic pronunciation quality evaluation model method, which further improved the efficiency and performance of pronunciation evaluation. Literature [16] constructs a pronunciation quality evaluation model independent of phonemes, and the scoring effect is better than other methods. Literature [17] applied the log a posteriori probability algorithm to linguistic rules, and the correlation coefficient between automatic scoring and manual scoring reached 0.795, which improved the evaluation performance by 9%. Literature [18] proposed an objective scoring algorithm of pronunciation quality based on elliptic model theory, which improved the accuracy and efficiency of pronunciation quality evaluation. Literature [19] proposed a new oral English pronunciation evaluation algorithm pass and applied it to the interactive English language learning system of Tsinghua University. After testing, the sentence correlation between pass and expert score is 0.66, which is better than other scoring algorithms. Literature [20] compares the pronunciation sentence to be tested with the speed, intonation, stress, intonation, and rhythm of the standard language in the corpus. It comprehensively evaluates the quality of oral pronunciation and achieves good results. These results provide strong support for the research of computer-aided pronunciation quality evaluation.The main problems in the study of English pronunciation quality evaluation are as follows. Due to different countries and regions, people’s pronunciation habits and pronunciation characteristics are also different. The foreign oral English pronunciation quality evaluation system or model cannot be directly applied to domestic English learners. The existing computer-aided language learning systems at home and abroad focus on word and grammar learning. It only uses 1 ∼ 2 evaluation indexes as the evaluation basis, which is difficult to give English learners an objective and reasonable score. If we want to build a fair and reasonable oral English pronunciation evaluation system, it is the basic requirement to check whether the tester’s pronunciation content is correct. At the same time, we should also comprehensively consider language information such as intonation, speed, pronunciation and intonation, stress, rhythm and fluency, as well as nonvoice information such as emotion and body movement. Even, it may involve special functions such as stress detection, grammatical error detection and correction, and spoken rhythm. The existing pronunciation evaluation obviously cannot meet the requirements. In recent years, with the rapid development of big data, cloud computing and artificial intelligence technology, deep neural network is a new machine learning theory. High speed and accuracy of speech recognition make it possible. How to use deep neural network brings new opportunities and challenges to the fields of speech recognition, pronunciation evaluation, and intelligent speech interaction. At present, the evaluation of pronunciation quality directly based on College Students’ oral English corpus is rare. Constructing the evaluation method of College English oral pronunciation quality is conducive to the quantification and improvement of College English teaching quality.


## 3. English Teaching Pronunciation Detection and Recognition Algorithm Based on Clustering and Improved SSD

### 3.1. SSD Algorithm Principle

SSD algorithm follows the idea of regression in Yolo algorithm, but it is different from Yolo algorithm, which only uses the highest-level feature for prediction. SSD algorithm uses multiscale feature for classification and position regression, and the effect of detecting small targets is obviously better than Yolo. SSD algorithm scales the original input signal to a fixed size, and the SSD network composed of improved vgg16 network and five cascaded convolution layers extracts the target feature. Then, it obtains multiscale features of 64 × 64, 32 × 32, 16 × 16, 8 × 8, 4 × 4, 2 × 2, and 1 × 1 pixels. A series of bounding box sets can be generated by setting different aspect ratio and different number of a priori boxes for different size feature. Given a priori frame, IOU matching can be carried out according to the dimension frame to obtain the calculation, as shown in the following equation:(1)FIoU=SG∩SHSG∩SH,where SG represents the area of the a priori box. SH represents the area of the real dimension box. FIoU represents the ratio of the intersection and union of the areas of the prediction frame and the real frame. When the IOU value of the true real box and the a priori box is greater than the threshold value of 0.5, the a priori box is retained. Moreover, the category of the a priori box is the category of the real box. All a priori frames are regressed, and the convolution kernel of 3 × 3 is used to obtain two parts of the predicted values of the category confidence and position offset of the a priori frame. Finally, the bounding box is adjusted by the maximum suppression algorithm to obtain the final positioning bounding box.


### 3.2. Improvement of SSD Algorithm

This paper proposes an improved SSD algorithm combined with improved perception module, multiscale feature fusion equalization network, and attention mechanism SENet to detect sound in complex environment. The methods of improvement are as follows: Based on the concept network, combined with the hole convolution of different hole rates, an improved concept module is proposed. It replaces conv8 layer, conv9 layer, and conv10 layer in SSD algorithm. This can speed up the calculation speed, increase the effective receptive field, and extract more sound feature information. However, due to the lack of good reuse of the low-level feature layer with rich sound information and the high-level feature layer with semantic information, this leads to the loss of information when part of the sound information is transmitted in multiple layers, so the feature information obtained by each layer is unbalanced. Based on the idea of feature pyramid network, this paper proposes a multiscale feature fusion equalization network to realize multilayer multiplexing and fusion equalization of sound features. After the SSD network feature extraction layer, it is connected to the attention mechanism SENet to recalibrate the feature channel weight. In this way, it can obtain more channel features of sound contained in the sound signal, enhance the feature extraction ability of the network, and further improve the detection accuracy of the model.In the cascade convolution layer added after VGG16 of the basic network, the SSD algorithm only uses 1 × 1 and 3 × 3 convolution kernels for one convolution. It is weak in feature extraction. An improved Inception module is proposed to enhance the feature extraction capability of an extra feature layer in AN SSD network. This module is composed of cavity convolution based on the improvement of Inception original network from Inception-V1 to Inception-V4. The improved Inception module network structure is shown in Figure1. In Figure 1, the cascade convolution layer and a maximum pooling layer are combined in parallel through three groups of convolution kernels of different sizes. It improves model performance by increasing network width. First of all, 1 × 1 convolution is used to reduce the dimension before and after the convolution operation, which greatly reduces the computational cost. Second, to extract more detailed features of the target and further improve the computing speed. From the perspective of deepening the nonlinear layer, the 5 × 5 convolution kernel is replaced by two 3 × 3 continuous convolution that can bring the same receptive field. Then, in order to solve the problem caused by using convolution kernels of different sizes to obtain receptive fields of different sizes, the convolution with different voids is introduced to replace the ordinary convolution in each parallel branch of ordinary convolution. Under the condition that the number of parameters is unchanged, the effective receptive fields are increased and the information of different receptive fields is fused to improve the feature representation ability of the target in the network. Finally, in order to retain more original feature information, a splicing branch was added by referring to residual structure to further improve model performance. In addition, the training speed and robustness of the network are accelerated through batch standardization operation and nonlinear mapping of ReLU activation function behind the convolutional layer. Compared with the convolutional layer of high-level feature extraction in SSD algorithm, the improved Inception module has stronger information extraction capability and speeds up network computation. This improved the recognition rate of medium targets, so this module replaced the Conv8, Conv9, and Conv10 layers in the SSD algorithm.Figure 1
Improved concept module network structure.Based on the multiscale feature formed by SSD algorithm from bottom to top, FPN adopts the top-down method to do double up sampling from the top-level feature. Moreover, it connects and superimposes the obtained feature layer by layer with the feature of the previous layer to form a new enhanced feature. Repeat the above steps to form a feature pyramid. The FPN structure is shown in the dotted box in Figure2. However, the long-distance information flow of FPN will lead to the loss of sound information. Because this fusion method focusing on adjacent resolution will cause the imbalance of sound feature information, this paper improves on the basis of FPN and proposes a multiscale fusion equalization network.Figure 2
Multiscale feature fusion equalization network structure.First, the feature of each layer formed by FPN network is transformed into the same scale according to a series of operations such as up sampling or down sampling. Then, the pixel level aggregate average processing is carried out. The aggregated averaged feature is formed into different scale feature through the opposite sampling operation, so as to obtain a multiscale feature with more balanced sound feature information. This can realize the multilayer multiplexing and fusion equalization of sound features, and improve the detection accuracy of small target sound.The specific implementation process of multiscale feature fusion equalization network is shown in Figure2. Based on the multiscale feature generated by SSD algorithm, considering the detection speed of the algorithm, only conv7 and conv8 are selected_ 2, Conv9_ 2, Conv10_ 2, Conv11_ The feature of layer 2 constitutes FPN and generates the fused feature {P1, P2, P3, P4, and P5}. Then, for P1 and P2, the maximum pool with step size of 4 and step size of 2 is used to generate the feature with the same resolution as P3. Two times up sampling and four times up sampling are performed on P4 and P5, respectively, to generate a feature with the same resolution as P3. Sum and average the transformed five feature of the same size to obtain the aggregated feature after a 3 × 3 to enhance the features. Finally, the characteristic after aggregating the mean value is obtained by four times up sampling and two times up sampling, respectively, and C4 and C5 are obtained by pooling the maximum value with step size of 2 and step size of 4, respectively. Thus, the same multiscale feature {C1, C2, C3, C4, and C5} as the previous FPN is obtained. Compared with the feature generated by FPN algorithm, the balanced semantic features with the same depth fusion are used to enhance the multilevel features. It enriches the sound semantic information and location information contained in each layer, and improves the recognition rate of small target sound.The SENet module mainly enables the model to pay attention to the relationship between channels. Through the model, we can automatically learn the importance of different channel characteristics and calibrate their importance. The attention mechanism of SENet can make the model pay more attention to the channel features with large amount of information and suppress those unimportant channel features. SENet can be divided into three stages: sequence, exception, and scale. The specific implementation method is shown in Figure3.Figure 3
Structure of SENet module.Firstly, the input characteristic i is convoluted to obtain the characteristic P. The feature p is compressed through the global average pooling operation to obtain the real number column with length C2. This operation enables the lower layer to use the information from the global receptive field of the network. The calculation equation is shown as follows:(2)kc=Fsqpc=1B×M∑y=1B∑y=1Mpcx,y,where B and M represent the height and width of the feature. pc represents the c-th channel of the characteristic. pcx,y represents the pixels in row x and column y of the c-th channel. kc is the output of extrusion operation.Then, the global features obtained by the squeeze operation are first reduced from C2 to 1/C2 through a full connection layer. Activate it with the ReLU function. Then, the dimension is upgraded to the original dimension C2 through a full connection layer. The sigmoid activation function is used to obtain the weight coefficients of each channel. The operation equation is shown as follows:(3)sc=Fexk,M=σak,M=σM2δM1k,where M1 and M2 are full connection operation. K is the output of extrusion operation. δ activates the function for ReLU. σ activates the function for sigmoid. sc is the output of excitation operation.Finally, the weight coefficient obtained by the exception operation is multiplied by the featureu to recalibrate the importance of the feature, so as to update the feature. The calculation equation is shown as follows:(4)i¯c=Fscpc,sc=sc×pc,where sc is the weight of the c-th channel of the characteristic graph. i¯c is the output of the weighting operation.Since the parameters added by SENet itself mainly come from two full connection layers, which has little impact on the real-time performance of the algorithm, this paper adds SENet after each feature layer.


### 3.3. Default Parameter Setting Based on K-Means Cluster Analysis

This model uses the fusion features output by the multiscale feature fusion module to predict the target. It needs to preset the default target frame parameters on each fusion feature before model training. SSD model uses empirical equation to set default target frame parameters. However, the empirical equation used by SSD is derived from natural signals and does not fit well with the target distribution characteristics in English pronunciation signals. This is one of the reasons why SSD model is difficult to achieve satisfactory results when it is directly applied to speech signal target detection.When the global target box K-means clustering method is used to preset the target box size, it is easy to lead to the clustering result biased to the side with large sample size. Based on the idea of preset target frame parameters by clustering method, this paper adopts a classified K-means clustering analysis method to obtain more representative default target frame parameters.Figure4 shows the relationship between the number of clusters K value and the average coverage of the target obtained by K-means cluster analysis of the target frame of the samples in the training data set of the data set. As can be seen from Figure 4, with the increase of the number of clusters, the change of average coverage tends to be stable. According to the trend of the function, the critical point at which the change of the average coverage of the target begins to stabilize is near the number of clusters k = 8. Therefore, we can think that the clustering result is ideal when the number of clusters k > 8.Figure 4
Broken line statistics of K-means clustering in two types.After further cluster analysis, the target frame clustering results whenk = 8 and K = 15 are selected for consonants and vowels, respectively, and the final 23 default target frame parameters are obtained. They are [5,10], [10,6], [9,11], [9,18], [20,11], [21,20], [14,21], [36,37], [47,48], [28,29], [56,67], [41,39], [18,18], [44,61], [142135], [68,67], [90,69], [94,93], [77,84], [55,54], [108110], [178174], and [254256]. Table 1 shows the quantitative evaluation results of the target size average coverage of the default target frame parameters obtained by SSD empirical equation, global clustering, and classification clustering methods.Table 1
Average coverage contrast of default parameter setting results (%).
Default methodsVowelConsonantAverage coverageEmpirical equation of SSD model80.3336.2958.31Global K-means clustering74.0885.0182.95Classification K-means clustering84.1182.4283.57It can be seen from Table1 that the default target frame parameters are obtained by classification clustering method, and the average coverage of vowel and consonant targets is better than 80%. It shows that the default target frame parameters obtained by this method can better reflect the distribution of vowels and consonants in the data set. As can be seen from Table 2, the K-means clustering method of classification avoids the problem that the clustering results tend to the party with a large number of samples under the condition of ensuring a certain increase in the average coverage. The clustering results can fit the distribution characteristics of consonant and vowel targets at the same time, and can obtain higher and more balanced average coverage of targets.Table 2
The pronunciation accuracy.
Scoring characteristicsLog of posteriori probabilityGOPLog of posteriori probability + GOPSentence level0.570.60.62Reader level0.740.760.85Conv4 is finally selected for this module_3, Conv5_3, FC7, Conv6_2, Conv7_2, Conv8_2, and conv9_2 seven layer features are used for subsequent target prediction At the same time, the distribution range of the default target frame of SSD model on each feature layer is used, and the classification clustering results are set on each feature.


### 3.4. Framework Design of English Teaching Pronunciation Detection System

The framework of English teaching pronunciation detection system is shown in Figure5. The voice acquisition module collects English reading voice, converts the input into voice digital signal, and transmits it to the controller. The controller transmits voice digital signals to the server based on the communication module. The server recognizes the voice digital signal and compares it with the standard English pronunciation data information prestored in the server. Then, the evaluation result can be obtained and transmitted to the management terminal. The storage module, touch screen display module, and voice playback module are connected with the controller. The storage module is responsible for storing English reading voice digital signals. The function of touch screen display module is to manually input information for readers and visually display the evaluation results. The function of the voice playback module is to play the voice digital signal in the storage module.Figure 5
The system framework.


## 3.1. SSD Algorithm Principle

SSD algorithm follows the idea of regression in Yolo algorithm, but it is different from Yolo algorithm, which only uses the highest-level feature for prediction. SSD algorithm uses multiscale feature for classification and position regression, and the effect of detecting small targets is obviously better than Yolo. SSD algorithm scales the original input signal to a fixed size, and the SSD network composed of improved vgg16 network and five cascaded convolution layers extracts the target feature. Then, it obtains multiscale features of 64 × 64, 32 × 32, 16 × 16, 8 × 8, 4 × 4, 2 × 2, and 1 × 1 pixels. A series of bounding box sets can be generated by setting different aspect ratio and different number of a priori boxes for different size feature. Given a priori frame, IOU matching can be carried out according to the dimension frame to obtain the calculation, as shown in the following equation:(1)FIoU=SG∩SHSG∩SH,where SG represents the area of the a priori box. SH represents the area of the real dimension box. FIoU represents the ratio of the intersection and union of the areas of the prediction frame and the real frame. When the IOU value of the true real box and the a priori box is greater than the threshold value of 0.5, the a priori box is retained. Moreover, the category of the a priori box is the category of the real box. All a priori frames are regressed, and the convolution kernel of 3 × 3 is used to obtain two parts of the predicted values of the category confidence and position offset of the a priori frame. Finally, the bounding box is adjusted by the maximum suppression algorithm to obtain the final positioning bounding box.


## 3.2. Improvement of SSD Algorithm

This paper proposes an improved SSD algorithm combined with improved perception module, multiscale feature fusion equalization network, and attention mechanism SENet to detect sound in complex environment. The methods of improvement are as follows: Based on the concept network, combined with the hole convolution of different hole rates, an improved concept module is proposed. It replaces conv8 layer, conv9 layer, and conv10 layer in SSD algorithm. This can speed up the calculation speed, increase the effective receptive field, and extract more sound feature information. However, due to the lack of good reuse of the low-level feature layer with rich sound information and the high-level feature layer with semantic information, this leads to the loss of information when part of the sound information is transmitted in multiple layers, so the feature information obtained by each layer is unbalanced. Based on the idea of feature pyramid network, this paper proposes a multiscale feature fusion equalization network to realize multilayer multiplexing and fusion equalization of sound features. After the SSD network feature extraction layer, it is connected to the attention mechanism SENet to recalibrate the feature channel weight. In this way, it can obtain more channel features of sound contained in the sound signal, enhance the feature extraction ability of the network, and further improve the detection accuracy of the model.In the cascade convolution layer added after VGG16 of the basic network, the SSD algorithm only uses 1 × 1 and 3 × 3 convolution kernels for one convolution. It is weak in feature extraction. An improved Inception module is proposed to enhance the feature extraction capability of an extra feature layer in AN SSD network. This module is composed of cavity convolution based on the improvement of Inception original network from Inception-V1 to Inception-V4. The improved Inception module network structure is shown in Figure1. In Figure 1, the cascade convolution layer and a maximum pooling layer are combined in parallel through three groups of convolution kernels of different sizes. It improves model performance by increasing network width. First of all, 1 × 1 convolution is used to reduce the dimension before and after the convolution operation, which greatly reduces the computational cost. Second, to extract more detailed features of the target and further improve the computing speed. From the perspective of deepening the nonlinear layer, the 5 × 5 convolution kernel is replaced by two 3 × 3 continuous convolution that can bring the same receptive field. Then, in order to solve the problem caused by using convolution kernels of different sizes to obtain receptive fields of different sizes, the convolution with different voids is introduced to replace the ordinary convolution in each parallel branch of ordinary convolution. Under the condition that the number of parameters is unchanged, the effective receptive fields are increased and the information of different receptive fields is fused to improve the feature representation ability of the target in the network. Finally, in order to retain more original feature information, a splicing branch was added by referring to residual structure to further improve model performance. In addition, the training speed and robustness of the network are accelerated through batch standardization operation and nonlinear mapping of ReLU activation function behind the convolutional layer. Compared with the convolutional layer of high-level feature extraction in SSD algorithm, the improved Inception module has stronger information extraction capability and speeds up network computation. This improved the recognition rate of medium targets, so this module replaced the Conv8, Conv9, and Conv10 layers in the SSD algorithm.Figure 1
Improved concept module network structure.Based on the multiscale feature formed by SSD algorithm from bottom to top, FPN adopts the top-down method to do double up sampling from the top-level feature. Moreover, it connects and superimposes the obtained feature layer by layer with the feature of the previous layer to form a new enhanced feature. Repeat the above steps to form a feature pyramid. The FPN structure is shown in the dotted box in Figure2. However, the long-distance information flow of FPN will lead to the loss of sound information. Because this fusion method focusing on adjacent resolution will cause the imbalance of sound feature information, this paper improves on the basis of FPN and proposes a multiscale fusion equalization network.Figure 2
Multiscale feature fusion equalization network structure.First, the feature of each layer formed by FPN network is transformed into the same scale according to a series of operations such as up sampling or down sampling. Then, the pixel level aggregate average processing is carried out. The aggregated averaged feature is formed into different scale feature through the opposite sampling operation, so as to obtain a multiscale feature with more balanced sound feature information. This can realize the multilayer multiplexing and fusion equalization of sound features, and improve the detection accuracy of small target sound.The specific implementation process of multiscale feature fusion equalization network is shown in Figure2. Based on the multiscale feature generated by SSD algorithm, considering the detection speed of the algorithm, only conv7 and conv8 are selected_ 2, Conv9_ 2, Conv10_ 2, Conv11_ The feature of layer 2 constitutes FPN and generates the fused feature {P1, P2, P3, P4, and P5}. Then, for P1 and P2, the maximum pool with step size of 4 and step size of 2 is used to generate the feature with the same resolution as P3. Two times up sampling and four times up sampling are performed on P4 and P5, respectively, to generate a feature with the same resolution as P3. Sum and average the transformed five feature of the same size to obtain the aggregated feature after a 3 × 3 to enhance the features. Finally, the characteristic after aggregating the mean value is obtained by four times up sampling and two times up sampling, respectively, and C4 and C5 are obtained by pooling the maximum value with step size of 2 and step size of 4, respectively. Thus, the same multiscale feature {C1, C2, C3, C4, and C5} as the previous FPN is obtained. Compared with the feature generated by FPN algorithm, the balanced semantic features with the same depth fusion are used to enhance the multilevel features. It enriches the sound semantic information and location information contained in each layer, and improves the recognition rate of small target sound.The SENet module mainly enables the model to pay attention to the relationship between channels. Through the model, we can automatically learn the importance of different channel characteristics and calibrate their importance. The attention mechanism of SENet can make the model pay more attention to the channel features with large amount of information and suppress those unimportant channel features. SENet can be divided into three stages: sequence, exception, and scale. The specific implementation method is shown in Figure3.Figure 3
Structure of SENet module.Firstly, the input characteristic i is convoluted to obtain the characteristic P. The feature p is compressed through the global average pooling operation to obtain the real number column with length C2. This operation enables the lower layer to use the information from the global receptive field of the network. The calculation equation is shown as follows:(2)kc=Fsqpc=1B×M∑y=1B∑y=1Mpcx,y,where B and M represent the height and width of the feature. pc represents the c-th channel of the characteristic. pcx,y represents the pixels in row x and column y of the c-th channel. kc is the output of extrusion operation.Then, the global features obtained by the squeeze operation are first reduced from C2 to 1/C2 through a full connection layer. Activate it with the ReLU function. Then, the dimension is upgraded to the original dimension C2 through a full connection layer. The sigmoid activation function is used to obtain the weight coefficients of each channel. The operation equation is shown as follows:(3)sc=Fexk,M=σak,M=σM2δM1k,where M1 and M2 are full connection operation. K is the output of extrusion operation. δ activates the function for ReLU. σ activates the function for sigmoid. sc is the output of excitation operation.Finally, the weight coefficient obtained by the exception operation is multiplied by the featureu to recalibrate the importance of the feature, so as to update the feature. The calculation equation is shown as follows:(4)i¯c=Fscpc,sc=sc×pc,where sc is the weight of the c-th channel of the characteristic graph. i¯c is the output of the weighting operation.Since the parameters added by SENet itself mainly come from two full connection layers, which has little impact on the real-time performance of the algorithm, this paper adds SENet after each feature layer.


## 3.3. Default Parameter Setting Based on K-Means Cluster Analysis

This model uses the fusion features output by the multiscale feature fusion module to predict the target. It needs to preset the default target frame parameters on each fusion feature before model training. SSD model uses empirical equation to set default target frame parameters. However, the empirical equation used by SSD is derived from natural signals and does not fit well with the target distribution characteristics in English pronunciation signals. This is one of the reasons why SSD model is difficult to achieve satisfactory results when it is directly applied to speech signal target detection.When the global target box K-means clustering method is used to preset the target box size, it is easy to lead to the clustering result biased to the side with large sample size. Based on the idea of preset target frame parameters by clustering method, this paper adopts a classified K-means clustering analysis method to obtain more representative default target frame parameters.Figure4 shows the relationship between the number of clusters K value and the average coverage of the target obtained by K-means cluster analysis of the target frame of the samples in the training data set of the data set. As can be seen from Figure 4, with the increase of the number of clusters, the change of average coverage tends to be stable. According to the trend of the function, the critical point at which the change of the average coverage of the target begins to stabilize is near the number of clusters k = 8. Therefore, we can think that the clustering result is ideal when the number of clusters k > 8.Figure 4
Broken line statistics of K-means clustering in two types.After further cluster analysis, the target frame clustering results whenk = 8 and K = 15 are selected for consonants and vowels, respectively, and the final 23 default target frame parameters are obtained. They are [5,10], [10,6], [9,11], [9,18], [20,11], [21,20], [14,21], [36,37], [47,48], [28,29], [56,67], [41,39], [18,18], [44,61], [142135], [68,67], [90,69], [94,93], [77,84], [55,54], [108110], [178174], and [254256]. Table 1 shows the quantitative evaluation results of the target size average coverage of the default target frame parameters obtained by SSD empirical equation, global clustering, and classification clustering methods.Table 1
Average coverage contrast of default parameter setting results (%).
Default methodsVowelConsonantAverage coverageEmpirical equation of SSD model80.3336.2958.31Global K-means clustering74.0885.0182.95Classification K-means clustering84.1182.4283.57It can be seen from Table1 that the default target frame parameters are obtained by classification clustering method, and the average coverage of vowel and consonant targets is better than 80%. It shows that the default target frame parameters obtained by this method can better reflect the distribution of vowels and consonants in the data set. As can be seen from Table 2, the K-means clustering method of classification avoids the problem that the clustering results tend to the party with a large number of samples under the condition of ensuring a certain increase in the average coverage. The clustering results can fit the distribution characteristics of consonant and vowel targets at the same time, and can obtain higher and more balanced average coverage of targets.Table 2
The pronunciation accuracy.
Scoring characteristicsLog of posteriori probabilityGOPLog of posteriori probability + GOPSentence level0.570.60.62Reader level0.740.760.85Conv4 is finally selected for this module_3, Conv5_3, FC7, Conv6_2, Conv7_2, Conv8_2, and conv9_2 seven layer features are used for subsequent target prediction At the same time, the distribution range of the default target frame of SSD model on each feature layer is used, and the classification clustering results are set on each feature.


## 3.4. Framework Design of English Teaching Pronunciation Detection System

The framework of English teaching pronunciation detection system is shown in Figure5. The voice acquisition module collects English reading voice, converts the input into voice digital signal, and transmits it to the controller. The controller transmits voice digital signals to the server based on the communication module. The server recognizes the voice digital signal and compares it with the standard English pronunciation data information prestored in the server. Then, the evaluation result can be obtained and transmitted to the management terminal. The storage module, touch screen display module, and voice playback module are connected with the controller. The storage module is responsible for storing English reading voice digital signals. The function of touch screen display module is to manually input information for readers and visually display the evaluation results. The function of the voice playback module is to play the voice digital signal in the storage module.Figure 5
The system framework.


## 4. Experiment and Analysis

### 4.1. Data Configuration

Forty college students’ English reading pronunciation was selected as the acoustic adaptive model corpus, and all students selected 20 sentences in the corpus. The students try to read aloud at a standard level. The English reading pronunciation database mainly contains the pronunciation of 70 college students. The number of male and female students is the same, but there are significant differences in pronunciation level. All students read 20 sentences from the corpus, which are composed of about 10 words. Invite three experienced English experts as judges to evaluate the pronunciation quality according to the pronunciation accuracy, fluency, and integrity. The standard score is 0–10. The average score of experts is the manual score of all speech.


### 4.2. Analysis of Manual Scoring Results

Manual scoring is an important basis for machine scoring, and the consistency needs to be evaluated first. Take the open correlation as the evaluation index and set the one withM scores, the score calculation is as follows:(5)Ffu=nfu,1M−1∑v≠ufv.fu represents the score vector of English experts. n∗,∗ represents the correlation degree of two scoring vectors.(6)nX,Y=∑i=1tXi−X′Yi−Y′∑i=1tXi−X′2∑i=1tYi−Y′2.t represents the vector dimension. Xi and Yi represent the i-th value of scoring vectors X and Y. X′, Y′ represents the mean value of X and Y. The scoring results of expert consistency are shown in Table 3.Table 3
The manual scoring results.
English expertSentence levelReader levelA0.790.95B0.850.99C0.830.94Mean value0.830.96As can be seen from Table3, the average values of sentence level and Reader level are 0.83 and 0.96. This shows that the consistency of manual scoring is good and can be used as the main carrier of machine scoring.


### 4.3. Machine Scoring Performance Test Results

Randomly select 20% of the speech database as the test set and the others as the training set. Calculate the correlation between the test results and the manual scoring results, and select the average correlation degree of 50% cross validation to measure and analyze the scoring performance of the machine. Test the scoring characteristics, and use the SVR scoring model fusion analysis to test the comprehensive performance through experiments. The experimental results of pronunciation accuracy are shown in Table2.It can be seen from Table2 that log posterior probability and Goodness of Pronunciation (GOP) are the best accuracy evaluation standards, and good evaluation performance can be obtained by independent use. In particular, GOP has the best performance. Combined with the two, the pronunciation accuracy can be significantly improved, and the correlation between sentence level and Reader level can be significantly improved.The results of pronunciation fluency experiment are shown in Figure6.Figure 6
The result of pronunciation fluency.It can be seen from Figure6 that the performance of speed evaluation is the best in fluency, indicating that students with higher pronunciation level speak faster and read fluently, so the manual score is higher. When the characteristics of speech speed and pause time are combined, the pronunciation fluency is significantly improved.The results of reading integrity experiment are shown in Table4.Table 4
The result of reading integrity.
Scoring characteristicsWord matching degreeSentence level0.55Reader level0.71It can be seen from Table4 that the evaluation of reading integrity by word matching can obtain good evaluation performance, and the correlation between sentence level and Reader level is relatively high.Under the combination of accuracy, fluency, and integrity, the experimental evaluation results of English reading pronunciation quality are shown in Figure7.Figure 7
The pronunciation quality of reading aloud.It can be seen from Figure7 that the proportion of pronunciation accuracy and fluency is relatively large, and the evaluation performance of accuracy is better than fluency. The evaluation of pronunciation quality is based on pronunciation accuracy, fluency, and integrity, and good evaluation performance is obtained. The correlation between sentence level and Reader level is significantly improved compared with pronunciation accuracy.


## 4.1. Data Configuration

Forty college students’ English reading pronunciation was selected as the acoustic adaptive model corpus, and all students selected 20 sentences in the corpus. The students try to read aloud at a standard level. The English reading pronunciation database mainly contains the pronunciation of 70 college students. The number of male and female students is the same, but there are significant differences in pronunciation level. All students read 20 sentences from the corpus, which are composed of about 10 words. Invite three experienced English experts as judges to evaluate the pronunciation quality according to the pronunciation accuracy, fluency, and integrity. The standard score is 0–10. The average score of experts is the manual score of all speech.


## 4.2. Analysis of Manual Scoring Results

Manual scoring is an important basis for machine scoring, and the consistency needs to be evaluated first. Take the open correlation as the evaluation index and set the one withM scores, the score calculation is as follows:(5)Ffu=nfu,1M−1∑v≠ufv.fu represents the score vector of English experts. n∗,∗ represents the correlation degree of two scoring vectors.(6)nX,Y=∑i=1tXi−X′Yi−Y′∑i=1tXi−X′2∑i=1tYi−Y′2.t represents the vector dimension. Xi and Yi represent the i-th value of scoring vectors X and Y. X′, Y′ represents the mean value of X and Y. The scoring results of expert consistency are shown in Table 3.Table 3
The manual scoring results.
English expertSentence levelReader levelA0.790.95B0.850.99C0.830.94Mean value0.830.96As can be seen from Table3, the average values of sentence level and Reader level are 0.83 and 0.96. This shows that the consistency of manual scoring is good and can be used as the main carrier of machine scoring.


## 4.3. Machine Scoring Performance Test Results

Randomly select 20% of the speech database as the test set and the others as the training set. Calculate the correlation between the test results and the manual scoring results, and select the average correlation degree of 50% cross validation to measure and analyze the scoring performance of the machine. Test the scoring characteristics, and use the SVR scoring model fusion analysis to test the comprehensive performance through experiments. The experimental results of pronunciation accuracy are shown in Table2.It can be seen from Table2 that log posterior probability and Goodness of Pronunciation (GOP) are the best accuracy evaluation standards, and good evaluation performance can be obtained by independent use. In particular, GOP has the best performance. Combined with the two, the pronunciation accuracy can be significantly improved, and the correlation between sentence level and Reader level can be significantly improved.The results of pronunciation fluency experiment are shown in Figure6.Figure 6
The result of pronunciation fluency.It can be seen from Figure6 that the performance of speed evaluation is the best in fluency, indicating that students with higher pronunciation level speak faster and read fluently, so the manual score is higher. When the characteristics of speech speed and pause time are combined, the pronunciation fluency is significantly improved.The results of reading integrity experiment are shown in Table4.Table 4
The result of reading integrity.
Scoring characteristicsWord matching degreeSentence level0.55Reader level0.71It can be seen from Table4 that the evaluation of reading integrity by word matching can obtain good evaluation performance, and the correlation between sentence level and Reader level is relatively high.Under the combination of accuracy, fluency, and integrity, the experimental evaluation results of English reading pronunciation quality are shown in Figure7.Figure 7
The pronunciation quality of reading aloud.It can be seen from Figure7 that the proportion of pronunciation accuracy and fluency is relatively large, and the evaluation performance of accuracy is better than fluency. The evaluation of pronunciation quality is based on pronunciation accuracy, fluency, and integrity, and good evaluation performance is obtained. The correlation between sentence level and Reader level is significantly improved compared with pronunciation accuracy.


## 5. Conclusion

Oral English learning and dialogue and communication are indispensable links in the whole process of English learning. The key to mastering this language is to use accurate English pronunciation and improve the quality of oral English pronunciation. Aiming at the poor effect of the original SSD algorithm on English teaching pronunciation detection, this paper proposes an English teaching pronunciation detection and recognition algorithm based on clustering and improved SSD. By enhancing the concept network module to replace the convolution layer of SSD network, the parameters of the model are reduced. In order to improve the recognition rate, a multiscale feature fusion equalization network is designed. The integration mechanism of attention and equalization are added. More accurate default parameters are obtained through K-means clustering analysis, so as to effectively improve the ability of the network to extract target information. Through the final experimental comparison, it is obvious that this algorithm has more advantages than the original SSD algorithm. In the experiment, three experienced English experts were invited as judges to evaluate the pronunciation quality according to the pronunciation accuracy, pronunciation fluency, and reading integrity. The results show that the system can accurately evaluate the pronunciation quality of reading aloud.



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*Source: 1626229-2022-05-11.xml*