English Speech Recognition Research Articles

Grammatical Error Correction Detection of English Conversational Pronunciation Under a Deep Learning Algorithm

Grammar correction in spoken English can enhance proficiency. This paper briefly introduces the gate recurrent unit (GRU) algorithm and its appli- cation in English speech recognition and grammatical error correction of speech recognition results. The GRU algorithm was firstly used to recognize English speech, then transform it into a text, and finally correct the English grammar of the text. Additionally, the attention mechanism was incorporated to enhance the performance of grammatical error correction. Subsequently, simulation experiments were conducted. Firstly, speech recognition and grammatical error correction were independently verified. The performance of the proposed algorithm in correcting grammatical errors in spoken English was evaluated using a self-built speech database. The results demonstrated that the proposed GRU-based algorithm yielded the best performance in independent speech recognition, independent grammatical error correction, and the overall spoken grammatical error correction. The contribution of this study lies in using the GRU algorithm to convert speech into text and perform grammar correction on the text, providing an effective reference for grammar correction in English communication.

Deep learning model of semantic direction exploration based on English V+able corpus distribution and semantic roles

In order to improve English learning efficiency, this paper constructs a deep learning model of semantic orientation exploration based on English V+able corpus distribution and semantic roles. This article combines the practical needs of English learning and establishes an ILP model with the optimization objective of minimizing spectrum resource occupation. A traffic grooming based time aware multipath RSA algorithm (HMRSA-TG) is proposed to solve the standardization problem of English speech recognition. To improve the system efficiency of intelligent English learning systems, a traffic grooming based time aware multipath RSA algorithm (HMRSA-TG) is proposed. Through research, it has been verified that the semantic orientation exploration deep learning model based on the distribution of semantic roles in English V+able corpora can effectively improve the effectiveness of English speech learning. The corpus model proposed in this paper can provide a reliable benchmark database for many speech problem learners and play an important role in English translation software in recognizing input speech with different accents

A PRISMA-driven Review of Speech Recognition based on English, Mandarin Chinese, Hindi and Urdu Language

Urdu Language ranks ten and is continuously progressing. This unique PRISMA-Driven review deeply investigates Urdu speech recognition literature and adjoin it with English, Mandarin Chinese, and Hindi languages frame-works conceptualizing wider global perspective. The main objective is to unify progress on classical Artificially Intelligent (AI) and recent Deep Neural Networks (DNN) based speech recognition pipeline encompassing Dataset challenges, Feature extraction methods, Experimental design and the smooth integration with both Acoustic models (AM) and Language models (LM) using Transcriptions. A total of 176 articles were extracted from Google Scholar database for each language with custom query design. Inclusion criteria and quality assessment leads to end up with 5 review and 42 research articles. Comparative research questions have been addressed and findings were organized by four possible speech types: Isolated, connected, continuous and spontaneous. The finding shows that English, Mandarin, and Hindi languages used spontaneous speech size of 300, 200 and 1108 hours respectively which is quite remarkable as compared to Urdu spontaneous speech data size of only 9.5 hours. For the same data size reason, the Word Error Rate (WER) for English falls below 5% while for Mandarin Chinese the alternative metric Character Error Rate (CER) is mostly used that lies below 25%. The success of English and Chinese Speech recognition leads to incomparable accuracy due to wide use of DNNs like Conformer, Transformers, E2E-attention in comparison to conventional feature extraction and AI models LSTM, TDNN, RNN, HMM, GMM-HMM; used frequently by both Hindi and Urdu.

Online learning system for English speech automatic recognition based on hidden Markov model algorithm and conditional random field algorithm

E-learning provides a flexible way of learning, which can provide corresponding learning content and tasks according to students’ interests, abilities and learning progress, so that students can choose learning content and learning progress more independently. In traditional teaching methods, teachers need to spend a lot of time and energy to evaluate students’ learning. The e-learning system can automatically record students’ learning behavior and learning results, and give feedback and evaluation in time during the learning process. Automatic speech recognition systems have been widely applied in various fields, and the demand for more user-friendly and convenient user operating systems is becoming increasingly urgent. This article proposes an English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm to improve recognition accuracy and stability, and applies it to the computer field. Label inference and optimization for speech recognition were carried out using the conditional random field algorithm. By establishing the correlation between features and labels, label inference was performed on the speech signal, which was compared with existing speech recognition systems to evaluate whether the recognition accuracy and stability of the new system had been improved. The results show that the English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm exhibits high performance in terms of recognition accuracy and stability. Compared with traditional speech recognition systems, this system has made significant improvements in the application of computer science.

A Study on Speech Recognition by a Neural Network Based on English Speech Feature Parameters

In this study, from the perspective of English speech feature parameters, two feature parameters, the mel-frequency cepstral coefficient (MFCC) and filter bank (Fbank), were selected to identify English speech. The algorithms used for recognition employed the classical back-propagation neural network (BPNN), recurrent neural network (RNN), and long short-term memory (LSTM) that were obtained by improving RNN. The three recognition algorithms were compared in the experiments, and the effects of the two feature parameters on the performance of the recognition algorithms were also compared. The LSTM model had the best identification performance among the three neural networks under different experimental environments; the neural network model using the MFCC feature parameter outperformed the neural network using the Fbank feature parameter; the LSTM model had the highest correct rate and the highest speed, while the RNN model ranked second, and the BPNN model ranked worst. The results confirm that the application of the LSTM model in combination with MFCC feature parameter extraction to English speech recognition can achieve higher speech recognition accuracy compared to other neural networks.

Development of the Children's English and Spanish Speech Recognition Test: Psychometric Properties, Feasibility, Reliability, and Normative Data.

The Children's English and Spanish Speech Recognition (ChEgSS) test is a computer-based tool for assessing closed-set word recognition in English and in Spanish, with a masker that is either speech-shaped noise or competing speech. The present study was conducted to (1) characterize the psychometric properties of the ChEgSS test, (2) evaluate feasibility and reliability for a large cohort of Spanish/English bilingual children with normal hearing, and (3) establish normative data. Three experiments were conducted to evaluate speech perception in children (4-17 years) and adults (19-40 years) with normal hearing using the ChEgSS test. In Experiment 1, data were collected from Spanish/English bilingual and English monolingual adults at multiple, fixed signal-to-noise ratios. Psychometric functions were fitted to the word-level data to characterize variability across target words in each language and in each masker condition. In Experiment 2, Spanish/English bilingual adults were tested using an adaptive tracking procedure to evaluate the influence of different target-word normalization approaches on the reliability of estimates of masked-speech recognition thresholds corresponding to 70.7% correct word recognition and to determine the optimal number of reversals needed to obtain reliable estimates. In Experiment 3, Spanish/English bilingual and English monolingual children completed speech perception testing using the ChEgSS test to (1) characterize feasibility across age and language group, (2) evaluate test-retest reliability, and (3) establish normative data. Experiments 1 and 2 yielded data that are essential for stimulus normalization, optimizing threshold estimation procedures, and interpreting threshold data across test language and masker type. Findings obtained from Spanish/English bilingual and English monolingual children with normal hearing in Experiment 3 support feasibility and demonstrate reliability for use with children as young as 4 years of age. Equivalent results for testing in English and Spanish were observed for Spanish/English bilingual children, contingent on adequate proficiency in the target language. Regression-based threshold norms were established for Spanish/English bilingual and English monolingual children between 4 and 17 years of age. The present findings indicate the ChEgSS test is appropriate for testing a wide age range of children with normal hearing in either Spanish, English, or both languages. The ChEgSS test is currently being evaluated in a large cohort of patients with hearing loss at pediatric audiology clinics across the United States. Results will be compared with normative data established in the present study and with established clinical measures used to evaluate English- and Spanish-speaking children. Questionnaire data from parents and clinician feedback will be used to further improve test procedures.

The impact of non-native English speakers’ phonological and prosodic features on automatic speech recognition accuracy

The present study examines the impact of Arab speakers’ phonological and prosodic features on the accuracy of automatic speech recognition (ASR) of non-native English speech. The authors first investigated the perceptions of 30 Egyptian ESL teachers and 70 Egyptian university students towards the L1 (Arabic)-based errors affecting intelligibility and then carried out a data analysis of the ASR of the students’ English speech to find out whether the errors investigated resulted in intelligibility breakdowns in an ASR setting. In terms of the phonological features of non-native speech, the results showed that the teachers gave more weight to pronunciation features of accented speech that did not actually hinder recognition, that the students were mostly oblivious to the L2 errors they made and their impact on intelligibility, and that L2 errors which were not perceived as serious by both teachers and students had negative impacts on ASR accuracy levels. In regard to the prosodic features of non-native speech, it was found that lower speech rates resulted in more accurate speech recognition levels, higher speech intensity led to less deletion errors, and voice pitch did not seem to have any impact on ASR accuracy levels. The study, accordingly, recommends training ASR systems with more non-native data to increase their accuracy levels as well as paying more attention to remedying non-native speakers’ L1-based errors that are more likely to impact non-native automatic speech recognition.

An Exploration of the Methods of Virtual Reality Technology to Enhance the Tourism Experience of Non-native English Speakers

Abstract This paper combines virtual reality technology with end-to-end speech recognition technology to design a digital tourism system to satisfy non-native English speakers (tourists) with the advantage of interactive projection technology. VRML technology is utilized to accelerate network interaction and piggyback on the construction of the digital tourism system. Particle swarm-based hybrid collision algorithm and end-to-end English speech recognition algorithm are proposed respectively to construct a real and complete tourism environment and convert English speech to meet the tourism needs of non-native English speakers. The performance advantages of the improved AABB-MPSO algorithm are detected by combining the exploration of the influence of the size of the search space on the search success rate and search time. Analyze the relationship between the number of modeling units and the recognition error rate of the end-to-end English recognition model with a joint CTC and attention mechanism, using spoken speech of free conversation in real scenes. The seven digital tourism experience dimensions of scenic integrity, scenic service quality, scenic authenticity, scenic infrastructure, degree of commercialization, English recognition and conversion, and cultural connotation embodiment are delineated to explore the key factors of tourism physical examination. Non-native English speakers’ evaluation of the digital tourism experience is generally high, and the mean value of each dimension is greater than “3”, which means that the digital tourism system designed in this paper can meet the tourism needs of non-native English speakers.

Research on the Reform of the Teaching Mode of Rural English Education Assistance Based on the Technical Support of Network Technology

Abstract Under the background of the development of network technology, this paper aims to promote rural English teaching and constructs an English teaching model that combines English recognition technology and rural teaching. The main process of speech recognition is examined by analyzing different speech recognition technologies. Using a deep learning network, an English speech recognition model has been established. Combined with the English acoustic features in the network data, fluency of English speech is evaluated. Data embedding is performed on the English sequences in the network, combined with the sequence probability in the English data, so as to determine whether the English speech is correct or not. The Eval value for the English recognition model based on deep learning is 5.49%, while the test value is 5.89%, as per the results. As the English dataset increases, so does the English recognition technique proposed in this paper, and the accuracy remains above 0.6, and when the dataset is 500, the speech recognition accuracy is 0.8. The teaching model that combines speech recognition techniques with English teaching improves students’ English to a certain extent.

Retracted: Optimization Simulation of English Speech Recognition Accuracy Based on Improved Ant Colony Algorithm

Retracted: Optimization Simulation of English Speech Recognition Accuracy Based on Improved Ant Colony Algorithm

Retracted: Emotional Interactive Simulation System of English Speech Recognition in Virtual Context

Retracted: Emotional Interactive Simulation System of English Speech Recognition in Virtual Context

Deep Neural Network-based Mixed Speech Recognition Technology for Chinese and English

In the field of human-computer interaction, the current more advanced speech recognition systems are all single speech recognition, and it is urgent to adopt new in-depth learning technology to improve the existing speech recognition system. In this context, this research is based on DNN and investigates mixed speech recognition techniques for both Chinese and English. A single speech recognition algorithm based on DNN is first investigated, and then a new hybrid Chinese and English speech recognition model is constructed by fusing the attention mechanism and CTC loss function. In the construction of the hybrid speech recognition model, the end-to-end model and Transformer framework are used to combine the monotonic alignment property of the CTC loss function, which allows complex sound units to be transformed into characters for easy extraction and recognition. The performance of the constructed models was tested on Chinese speech dataset, English speech dataset and mixed Chinese and English speech dataset to determine the recognition accuracy and speed of the models. The results show that the proposed recognition model achieves 81.2% recognition accuracy and 100 recognition speed/minute on the Chinese-English mixed speech dataset, which is much better than the other three models. This study successfully addresses the need for improved speech recognition systems by introducing a novel hybrid model for mixed Chinese-English speech recognition. The experimental results confirm the superiority of the proposed model, achieving high accuracy and rapid recognition speed. The developed model holds promising potential for enhancing human-computer interaction and enabling efficient communication between Chinese and English speakers.

Retracted: The Spoken English Practice System Based on Computer English Speech Recognition Technology

Retracted: The Spoken English Practice System Based on Computer English Speech Recognition Technology

Research on English speech recognition system and training enhancement based on bat algorithm and acoustic model inspection

Research on English speech recognition system and training enhancement based on bat algorithm and acoustic model inspection

Design of English teaching speech recognition system based on LSTM network and feature extraction

Design of English teaching speech recognition system based on LSTM network and feature extraction

Construction of English Speech Recognition Model by Fusing CNN and Random Deep Factorization TDNN

In current society, speech recognition can perform a variety of functions, such as completing voice commands, enabling speech processing, spoken language translation and facilitating communication. Therefore, the study of speech recognition technology is of high value. However, current speech recognition techniques focus on among clearly expressed spoken words, which poses great challenges for recognition with spoken pronunciation or dialect pronunciation. Some scholars currently use a model combining time-delay neural networks and long and short-term memory networks to build speech recognition systems, but the performance in acoustic recognition is poor. Therefore, the study proposes a convolutional neural network (CNN), time-delay neural network (TDNN) and output-gate projected Gated recurrent by analyzing the deep neural network unit (OPGRU) combined with a composite English speech recognition model. The model can optimize the acoustic model after the introduction of CNN, and the model can accurately recognize pronunciation features and make the model have a wider recognition range. The proposed composite model is compared with the Word error rate (Wer) and runtime metrics in the Mozilla Common Voice dataset. The Wer result of the composite model is 23.42% and the running time is 1418 s. The Wer result of the composite model is 24.61% and the running time is 1385 s. Compared with the TDNN-OPGRU model, the Wer of the composite model decreases by 1.19% but the running time increases by 33 s. The accuracy of the composite model is higher than that of the TDNN-OPGRU model. From a comprehensive consideration, the speech recognition model accuracy has higher priority, so the composite model proposed in the study has better performance.

Recognition of English speech – using a deep learning algorithm

Abstract The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated that the more training samples the speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same.

Retracted] Innovative Application of Sensor Combined with Speech Recognition Technology in College English Education in the Context of Artificial Intelligence

English listening is an effective way to improve students’ English expression ability and use oral communication. However, from the current situation of English teaching, the current English teaching methods are too single, and teachers do not focus on oral training in the classroom, resulting in low efficiency of classroom teaching. On the basis of following the principles of wholeness, interaction, balance, and sustainable development of educational ecology, by enhancing the synergy of ecological elements of English speaking classroom, promoting interactive dialogue among ecological subjects, and regulating classroom behaviors, it is conducive to giving full play to the advantageous role of information technology on English speaking teaching reform and promoting its sustainable development. This paper addresses the current situation of English listening teaching, especially the problem of reduced recognition rate of spoken language in noisy environment, and the principle of using dual‐sensor speech recognition system proposed. We design the speech recognition method based on recurrent neural network by acquiring the weak vibration pressure speech signal of the jaw skin and the speech signal transmitted through the air during the vocalization process through the sensor. Deep machine learning algorithm is used for speech recognition in English teaching. A reasonable frame sampling frequency is set to obtain the English speech signal, then the feature parameters representing this speech signal are obtained by linear prediction coefficients, and the speech feature vector is generated, followed by the recurrent neural network algorithm to train the speech features. In the related experiments, by comparing with the commonly used speech recognition algorithms, it is proved that the proposed algorithm English teaching speech recognition has higher accuracy and faster convergence.

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

[This retracts the article DOI: 10.1155/2022/7846877.].

Retracted] Design of Regression Model for MultiParameter Evaluation of English Pronunciation Quality

In order to better evaluate the quality of English pronunciation, this paper proposes a regression model design method based on multiparameter evaluation of English pronunciation quality. This method takes college students’ English pronunciation as the research object. Through the combination of various algorithms, a speech recognition model based on a machine learning neural network is constructed with characteristic parameters as input, and a speech quality evaluation model based on multiple regression is constructed with multiparameters such as intonation, speed, rhythm, and intonation as evaluation indicators. The experimental results show that log posterior probability and GOP are good measures of pronunciation standard. When used alone, a higher correlation with manual scores can be obtained, and the correlation of both exceeds 0.5. GOP has the best performance, with a correlation of 0.549. The combination of these two pronunciation standard evaluation features can further improve the evaluation performance, and the correlation degree reaches 0.574. Compared with the GOP algorithm with better performance, the evaluation performance is improved by 4.6%. Conclusion. The model provides a scientific basis for oral English speech recognition and objective evaluation of pronunciation quality.