Computer Aided Language Learning Research Articles

The Post Effect of Embedded Neural Network Mandarin Proficiency Test on Chinese Learners

In the context of rapid technological advancement, embedded neural network technology is gradually penetrating into the field of education, especially in computer-aided language learning (CALL) platforms, bringing unprecedented changes to Mandarin proficiency testing. This study focuses on the application of embedded neural network technology in Mandarin proficiency testing and its post effects on Chinese language learners, aiming to explore in depth how this emerging technology affects learners’ performance, motivation, and long-term learning outcomes. The core of the research lies in comparing the differences in learner performance, motivation, and subsequent learning behavior between computerized testing using embedded neural network technology and traditional paper-based testing. A comparative analysis of two testing modes was conducted by selecting Chinese learners with different language proficiency levels as samples. Preliminary results show that the Mandarin proficiency test based on embedded neural networks not only significantly improves learners’ learning motivation, but also demonstrates more significant results in improving listening and speaking skills due to its personalized teaching resources and intelligent feedback mechanism. This technology simulates real language environments and provides customized learning paths, effectively enhancing learners’ engagement and learning outcomes.

INTERDISCIPLINARY INTERACTIONS IN APPLIED LINGUISTICS: A CANADIAN PERSPECTIVE

The article analyzes applied linguistics as a field of knowledge in the Canadian experience and its interdisciplinary interactions. It is pointed out that interdisciplinary interaction with other sciences such as psychology, sociology, informatics, semiotics, cybernetics, etc. determined the evolution of linguistic research in recent decades. In the result of the integration of scientific efforts, such complex sciences as psycholinguistics, sociolinguistics, cognitive linguistics, computer linguistics, neurolinguistics, etc. have emerged. It is noted that the interdisciplinary interaction of applied linguistics contributes to its formation as a field of knowledge, enriching research and solving complex linguistic problems. It is established that the connection of applied linguistics with other scientific fields contributed to a comprehensive understanding of language and its use in various fields. The nature, advantages and influence of interdisciplinary connections of applied linguistics in Canada with other fields of knowledge are analyzed. In particular, the connection of applied linguistics with psychology offers an understanding of the cognitive processes involved in language acquisition. At their intersection, the subfield of psycholinguistics emerges. Leading researchers have identified psycholinguistics as the study of how people perceive, create, and understand language. It is noted that the interdisciplinary connection of applied linguistics and sociology solves the problems of language identity, diversity and the influence of social variables on language learning and use. It is determined that the connection of applied linguistics with information technologies, in particular with computational linguistics, contributes to the progress of computer-aided language learning (CALL) and innovative approaches to language learning. It is highlighted that interdisciplinary collaborations in applied linguistics have many advantages, such as a deeper and more thorough comprehension of language-related problems, creative approaches to research, and the creation of workable solutions to real-world language problems.

A novel framework for mispronunciation detection of Arabic phonemes using audio-oriented transformer models

Computer-Aided Language Learning (CALL) is experiencing notable growth in contemporary times due to the indispensability of acquiring proficiency in various languages for effective communication across diverse linguistic contexts. Within the domain of CALL, the inclusion of mispronunciation detection serves as an intrinsic component aimed at automatically pinpointing errors made by non-native speakers. In this research endeavor, a pioneering framework is put forth to address the task of detecting mispronunciations in Arabic phonemes through the utilization of audio-centric transformer models. To our current understanding, this represents the inaugural endeavor to comprehensively ascertain the mispronunciations of Arabic phonemes by employing audio-focused transformer models, including Squeezed and Efficient Wav2Vec (SEW), Hidden-Unit BERT (HUBERT), WAV2VEC, UNI-SPEECH. In order to demonstrate the effectiveness of the proposed model, a comprehensive evaluation is conducted on a set of 29 Arabic phonemes, including 8 hafiz sounds, uttered by 11 distinct individuals. For experimental purposes, two distinct versions of the dataset are utilized, incorporating additional voice samples obtained from the YouTube platform. The extensive experimental findings substantiate that employing the UNI-SPEECH transformer model yields notable classification outcomes in the context of Arabic phoneme mispronunciation detection. The systematic comparison of audio-centric transformer models adds valuable insights to the existing scholarly discourse and offers clarity regarding their effectiveness and appropriateness in the domain of Arabic phoneme mispronunciation detection. Additionally, the comprehensive comparative analysis of these transformer models unveils their respective advantages, constraints, and avenues for enhancement, thereby guiding future investigations in this realm.

Integrated, Enjoyable and Engaging L2 Acquisition through Computer-aided Language Learning

ELT

Challenges of Plateau State Polytechnic English Lecturers in Utilizing ICT for Manpower Development

Information Communication Technology (ICT) facilitates teaching and learning especially in the 21st century where online/digital resources have taken over the globe. The polytechnic is designed to train students who form the nation’s manpower. This paper examined the challenges faced by Plateau State Polytechnic English lecturers in utilizing ICT for manpower development. The study was necessitated by the call from educators around the world for the incorporation and effective utilization of online resources in teaching and learning. In order to investigate the challenges experienced by lecturers in the polytechnic over the utilization of ICT in manpower development through the teaching of English, a Focus Group Discussion (FGD) was conducted on the departmental WhatsApp platform of the Department of General Studies of the polytechnic consisting of eighteen English lecturers. The study adopted Computer-Aided Language Learning (CALL) as its framework. The result of the FCD revealed among others that there were so many problems hampering the utilization of ICT in the teaching and learning in the institution. These challenges principally are related to data infrastructure, knowledge infrastructure and management infrastructure. The study concluded that there was need to put in place ICT facilities in the polytechnic for the lecturers to utilize in the teaching of English being the central language of communication in the development of the nation’s manpower. Key words: Challenges, ICT, Manpower, Polytechnic, Utilization.

Computer-Aided Teaching and Assessment of Reading Skills in English as a Second Language

The research article presents the observations and findings of a case study on the effectiveness that the computeraided teaching of reading skill in English as a second language of first year engineering students. The research focuses on the students’ reading skills such as concentration on the text, transforming information, perception of meaning in the content, reasoning, and retrieval of required information from text. The present research investigates the change in language abilities deduced from the reading in the context of delivery of reading units in English Proficiency course. The study was conducted during two academic semesters of first year engineering for the academic year 2021-22. The target group consists of four batches of twenty five students having Marathi as their mother-tongue. The study has been carried out with the help of a computer based language laboratory multimedia instructional tool having the standard functions in it. The Reading Skill units of the course were taught in the traditional classroom approach to the control group and the experimental group has received the same content in computer assisted language laboratory software tool that enhances teaching-learning experience. The analysis of study is based on the formation and implementation of assessment tests on a CG and an EG. The result analysis directs the researcher to conclude that the computer-aided language learning tools generate positive change in students’ perception, transcoding, reasoning, and information recalling abilities. Keywords—Second language, Language proficiency, Reading Skills, Teaching-learning techniques, Computer-assisted learning.

English Speech Recognition System Model Based on Computer-Aided Function and Neural Network Algorithm.

With the economic globalization continuous growth of China's socioeconomic level tends to be internationalized, China's attention to English has been significantly improved. However, the domestic English teaching level is limited, so it is impossible to correct students' English pronunciation and make a reasonable evaluation at all times so that oral training has certain disadvantages. However, the computer-aided language learning system at home and abroad focuses on the practice of words and grammar, and the evaluation indicators are less and not comprehensive. In view of the complexity of English pronunciation changes, traditional speech recognition is difficult to recognize speech speed and improve its accuracy. Furthermore, to strengthen the English pronunciation of domestic students, a nonlinear network structure is studied in depth to simulate the human brain to analyze a model of speech recognition is established Mel frequency cepstrum characteristic parameters of human ear model and deep belief network. In this paper, the traditional computer pronunciation evaluation method is improved in an all-round way, and a set of high-quality speech recognition system of speech recognition method is constructed. Aiming at the above problems, it takes the students as the research, which proves that the method adopted in this paper can give the learners accurate pronunciation quality analysis report and guidance and correct their intonation and improve the learning effect, and the experimental data verify that the improved speech recognition system model recognition ability is higher than the traditional model.

On practical aspects of using of the frame-based sentence patterns in integrative computer-aided language learning system

The given work is devoted to resolution of the problems appeared during the attempt to build an integrative system using frame-based patterns. The main problems resolved in the work are as follows. Words reordering, effective error handling, general rules of transformation, effective scenario description.

Computer-Aided Language Learning (CALL) used in the example of Sakarya University

Considering the ubiquity of computers and the Internet in this ever changing technological era, it is definitely normal that they would affect the language learning process. As an institution, Sakarya University is supposed to keep pace with the technology in every field. Thereupon, CALL started being used at English Language Preparatory Class of Sakarya University. Computer-Aided Language Learning (CALL) is the fulfilment of software and web-based programs to ease the learning of English. CALL can be a good supplementary tool it cannot only be used like traditional face-to-face instruction. An increased use of computers and the advent of the Internet in every field of modern lifestyle make computer-aided language learning irreplaceable in all aspects. Being new to those who were fond of the high technology and who were fed up with using books, CALL grabbed the students’ attention, which made the study easier. In the system, Computers were tools that students mainly utilized. Every student had a computer reserved for himself/herself. It was an amazing experience for those using books. In this study it is aimed to evaluate CALL implemented at English Language Preparatory Class of Sakarya University and assess the students’ performances. The data analysis method was used. The data were able to be managed and analyzed properly and effectively after the conclusions of the research. The success results of the Sakarya University preparatory class students were evaluated. The data utilized in the study are restricted to the period of three academic years. According to results CALL has been successfully applied in years when the software used was up-to-date. In line with the results, CALL is a useful tool that helps teachers to ease the language learning process.

A Spoken English Teaching System Based on Speech Recognition and Machine Learning

The learning model and environment are two major constraints on spoken English learning by Chinese learners. The maturity of computer-aided language learning brings a new opportunity to spoken English learners. Based on speech recognition and machine learning, this paper designs a spoken English teaching system, and determines the overall architecture and functional modules of the system according to the system’s functional demand. Specifically, MATLAB was adopted to realize speech recognition, and generate a speech recognition module. Combined with machine learning algorithm, a deep belief network (DBN)-support vector machine (SVM) model was proposed to classify and detect the errors in pronunciation; the module also scores the quality and corrects the errors in pronunciation. This model was extended to a speech evaluation module was created. Next, several experiments were carried out to test multiple attributes of the system, including the accuracy of pronunciation classification and error detection, recognition rates of different environments and vocabularies, and the real-timeliness of recognition. The results show that our system achieved good performance, realized the preset design goals, and satisfied the user demand. This research provides an important theoretical and practical reference to transforming English teaching method, and improving the spoken English of learners.

Detection of Mispronunciation in Non-native Speech Using Acoustic Model and Convolutional Recurrent Neural Networks

Computer-Aided Language Learning (CALL) frameworks have gained much popularity because of their adaptability, enabling students to refine their language abilities at their own pace. Much research has been done to help improve CALL systems and dig out the more suitable features for targeted Mandarin mispronunciation detection is still an open research area. The acoustic model of convolutional recurrent neural networks (CRNN) (CNN + LSTM) + connectivity time series classification (CTC) model is used to convert acoustic signals into pinyin label sequences. As many Chinese speech data sets were trained using this model, the initial and vowel pronunciation error rates were finally obtained. Therefore, through linguistic classification, mainly four pronunciation errors related to the native Spanish pronunciation habits are further discovered. Moreover, based on the final analysis obtained, some corresponding instructive suggestions for further international Chinese teaching from different aspects are also put forward. Apart from proposing practical suggestions from different perspectives for further Mandarin CALL international teaching according to the experiment results’ evaluations, this system still has room for further improvement.

English Pronunciation Standards Based on Multimodal Acoustic Sensors

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

Dilin tədrisində və öyrənilməsində kommunikativ və situativ dilin öyrədilməsinin rolu

In this article the role of communicative and situational language teaching approaches is discussed. It is also discussed that different learning purposes compared with the traditional language teaching and learning, for example, Computer-aided language teaching and learning is both a big challenge and also a benefit to educators, as well as to communicative language teaching and situational language teaching. Key words: Communicative Language Teaching, Situational Language Teaching, approach, linguistic competence, communicative proficiency, specific interaction, language-speaking environment and situations

English speech sound improvement system based on deep learning from signal processing to semantic recognition

With the global integration and the increasing level of China's internationalization, the demand of Chinese people for English learning is growing rapidly. At the same time, English has gradually become one of the most frequently used languages in the world economic exchanges and cultural information exchanges. After the material life demands of the members of the domestic society are fully satisfied, the learning and explanation of the corresponding knowledge content of the English language are gradually highly valued by the staff in various production fields. After AI proposed deep learning models, researchers also began to improve these models and use them in speech recognition. Through multi-layer nonlinear structure, the essential features are preserved and more abstract advanced features are extracted, so the recognition rate is improved. Computer-aided language learning will change the existing language teaching mode and learning environment, so that learners can learn independently anytime and anywhere. Deep learning can give learners accurate, objective and timely pronunciation evaluation and feedback guidance. It can also help learners find out the differences between their pronunciation and standard pronunciation through repeated listening and comparison, correct their pronunciation errors, and improve the efficiency of language learning. This paper constructs an English language improvement system based on deep learning. The experimental results show that the proposed method can effectively analyze the input speech signals and make corresponding feedback.

The Training Skills of College Students’ Oral English Based on the Computer-aided Language Learning Environment

According to a large number of graduate employment questionnaire survey, researchers found that today’s College Students’ oral expression ability has been far from meeting the needs of society. This situation has contributed to the employment difficulties of college students. Therefore, how to effectively cultivate the oral communication ability of college students has become a concern of various universities. The improvement of College Students’ oral ability will improve their employment rate. Based on the theoretical knowledge of spoken language learning, a computer-aided language learning system is proposed. This paper analyzes the advantages of CAI in language teaching. It has been proved that its application can effectively cultivate the ability of using spoken English of college students.

Talking Head-based L2 Pronunciation Training: Impact on Achievement Emotions, Cognitive Load, and Their Relationships with Learning Performance

ABSTRACT Second language (L2) pronunciation training has been a worldwide task. Although computer technology makes it possible to develop a talking head to teach pronunciation like a real language teacher, little is known about how a talking head may act on L2 learners’ emotional and cognitive learning process. We investigate L2 learners’ achievement emotions, cognitive load, and pronunciation learning performance in a computer-assisted pronunciation training (CAPT) system embedded with four conditions: audio only (AU), a human face (HF), a 3D talking head with front view (3Df), and a 3D talking head with both front and profile views (3D). Results showed that, with learning time went on, participants’ perceived anxiety, boredom, and pride increased while shame and hopelessness decreased and enjoyment kept stable. With 3D, participants’ anxiety increased the most and boredom increased the least. Moreover, 3D group also perceived the highest germane load and got the highest pronunciation learning performance. Furthermore, anxiety and shame correlated with learning performance positively while boredom correlated with it negatively; enjoyment and pride correlated positively with performance on Mandarin tones. These findings significantly contribute to the efforts to design or select virtual characters for computer-aided language learning (CALL) and also provide a valuable reference to study achievement emotions in HCI systems.

Improving Mispronunciation Detection of Arabic Words for Non-Native Learners Using Deep Convolutional Neural Network Features

Computer-Aided Language Learning (CALL) is growing nowadays because learning new languages is essential for communication with people of different linguistic backgrounds. Mispronunciation detection is an integral part of CALL, which is used for automatic pointing of errors for the non-native speaker. In this paper, we investigated the mispronunciation detection of Arabic words using deep Convolution Neural Network (CNN). For automated pronunciation error detection, we proposed CNN features-based model and extracted features from different layers of Alex Net (layers 6, 7, and 8) to train three machine learning classifiers; K-nearest neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). We also used a transfer learning-based model in which feature extraction and classification are performed automatically. To evaluate the performance of the proposed method, a comprehensive evaluation is provided on these methods with a traditional machine learning-based method using Mel Frequency Cepstral Coefficients (MFCC) features. We used the same three classifiers KNN, SVM, and RF in the baseline method for mispronunciation detection. Experimental results show that with handcrafted features, transfer learning-based method and classification based on deep features extracted from Alex Net achieved an average accuracy of 73.67, 85 and 93.20 on Arabic words, respectively. Moreover, these results reveal that the proposed method with feature selection achieved the best average accuracy of 93.20% than all other methods.

Speech Recognition of Oral English Teaching Based on Deep Belief Network

The oral English teaching faces several common problems: the teaching method is very inefficient, and the learners are poor in oral English. The development of computer-aided language learning offers a possible solution to these problems. Based on techniques of speech recognition, cloud computing and deep learning, this paper applies the deep belief network (DBN) to recognize the speeches in oral English teaching, and establishes a multi-parameter evaluation model for the pronunciation quality of oral English among college students. The model combines the merits of subjective and objective evaluations, and assesses the pronunciation from four aspects: pitch, speech rate, rhythm and intonation. Finally, the proposed model was verified through speech recognition and pronunciation evaluation experiments on 26 non-English majors from a college. The results show that the proposed evaluation model output credible results, which are consistent with those of experts, as evidenced by consistency, neighbourhood consistency and Pearson correlation coefficient. The research provides a feasible way to evaluate the oral English proficiency of learners, laying the basis for improving the teaching and learning efficiency of oral English.

音声分析・合成・認識技術を用いた多様な外国語教育支援

音声分析・合成・認識技術を用いた多様な外国語教育支援

The Development of Indonesian POS Tagging System for Computer-aided Independent Language Learning

Word processing tool is a basic need in learning a language. One of the word processors needed by a language learner is part of speech (POS) tagging. While many POS Tagging tools for Indonesian language have been developed, no systems have been addressed specifically for language learners. This paper presents a study on an Indonesian part of speech (POS) tagging system developed as one of word processing tools for language learners. We use resources from previous Indonesian POS tagging research, such as MorphInd for the morphological analysis and IPOSTagger for part of speech tagging. Objective and subjective tests are employed to evaluate this system. In the objective test the part of speech tagging results use a system model developed from IPOSTagger in combination with MorphInd as the morphological analyzer, and compared with the results of part of speech tagging produced from the original IPOSTagger system model. The results show that the part of speech tagging accuracy using this system model is higher than other models. For its subjective evaluation, Mean Opinion Score (MOS) is used to the 24 participating respondents. The MOS results obtained reach 3,61 for test-1, 3,87 for test-2, and 3,72 for test-3. From the results, we expect that this POS tagging system could be used to help language learners in their Indonesian language self-learning process.