Design of Chinese-English Wireless Simultaneous Interpretation System Based on Speech Recognition Technology

Liu, Fengzhen

doi:10.1155/2021/7346984

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…The module can automatically sense the frequency of a sound signal and eliminate the noise in the sound signal, a sound gain arithmetic unit inside the module can automatically increase or decrease the output level of the sound according to the frequency of an input audio frequency, and an attached control circuit of the module can shield and remove the noise. These two controllers compensate for the fact that the audio signal is too large or too small to input the conversion signal and keep it at the target level to minimize clipping [8].…”

Section: Audio Input Devicementioning

confidence: 99%

Medical voice question: answering interactive system based on speech recognition technology

Xin

Zhang

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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The traditional medical voice question and answer interactive system can not meet the core needs of patient consultation service, and the patient's satisfaction with the system answer is low, so the research of medical voice question and answer interactive system based on speech recognition technology is proposed. In the hardware of the system, the control core of the system is the MCU module, which controls the normal operation of the whole system, realizes the conversion of the analog signal and digital signal through the audio input device, designs the serial communication device to transmit the data information to the computer, and uses the speech recognition technology to realize the software part of the system. And through the system test, the medical voice question and answer interactive system based on speech recognition technology is compared with the traditional medical question and answer interactive system. The experimental results show that the average response time of the medical voice question and answer interaction system based on speech recognition technology to the user request is far less than the traditional system, there is no number of error requests, and the performance is better than the traditional system. Therefore, it can be proved that the performance of the medical question and answer interactive system based on speech recognition technology has reached the expected standard, and can meet the basic needs of users for medical consultation services.

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Section: Audio Input Devicementioning

confidence: 99%

Medical voice question: answering interactive system based on speech recognition technology

Xin

Zhang

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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“…Machine translation uses "direct translation," "syntax conversion," "semantic conversion," and other technical means to make the target language approach the source language as much as possible and try to maintain its fluency and accuracy (Song, 2021). At present, machine translation can be divided into the following three categories: rule-based machine translation, statistics-based machine translation, and neural network-based machine translation (Liu, 2021). In the early 1990s, due to the success of deep learning and neural networks in the field of artificial intelligence, machine translation introduced these new technologies, so statistical machine translation developed into neural machine translation (Xia, 2020).…”

Section: Introductionmentioning

confidence: 99%

Design of a Smart Teaching English Translation System Based on Big Data Machine Learning

Zhang,

Yu,

Gao

et al. 2023

International Journal of Web-Based Learning and Teaching Technologies

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In the context of artificial intelligence, the use of machine translation in English reading classroom teaching is a more common learning method. In traditional teaching methods, machine translation is more convenient and faster than human translation, but it often deviates from the original text in terms of grammar and sentence pattern. Based on the perspective of English reading class, this paper compares traditional and machine translation, and discusses the future development trend and influence mechanism of the current situation of using machine translation in English reading class under the effect of artificial intelligence.

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“…However, spoken English may often encounter some colloquial content or sentences. is requires the translators of English interpreting to master more historical information or humanistic feelings of English-speaking countries [4,5]. On important political or economic occasions, the speaker's English may have some colloquial or nonstandard pronunciation, which may be determined by living environment and cultural information.…”

Section: Introductionmentioning

confidence: 99%

English Interpretation Learning System Combining Cognitive Ability and Collaborative Filtering Algorithm

Ya-hong

2022

Mathematical Problems in Engineering

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In the context of globalization, English interpretation is the language hub for political and economic exchanges in different countries. English interpretation is a technology different from the English translation, which requires translation accuracy and real-time. Traditional English interpreting learning methods can only learn English grammar and some English language and cultural information. At the same time, traditional English interpreting learning methods are inefficient and cumbersome tasks. English interpreting participants also need to interpret accurately in real-time based on the speaker’s behavioral information and the cultural information contained in English. This puts forward more requirements for the learning of English interpreting learners. This research uses the collaborative filtering (CF) algorithm and the algorithm based on ConvLSTM cognitive ability to study the three characteristics of English grammar, behavioral information, and English cultural information in the English interpreting learning system. The CF algorithm can recommend effective English knowledge for English interpreting learners, and the ConvLSTM algorithm can interpret the effectiveness of this recommended knowledge. The research results show that the CF algorithm and the ConvLSTM algorithm have good performance in recommending and predicting the grammar, behavioral information, and cultural characteristics of the English interpreting learning system. The largest similarity index reached 0.97, and the smallest similarity index also reached 0.93. ConvLSTM can better predict the changing trend and data value size of English interpretation-related feature data, and the largest prediction error is only 2.48%.

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Design of Chinese-English Wireless Simultaneous Interpretation System Based on Speech Recognition Technology

Cited by 4 publications

References 9 publications

Medical voice question: answering interactive system based on speech recognition technology

Medical voice question: answering interactive system based on speech recognition technology

Design of a Smart Teaching English Translation System Based on Big Data Machine Learning

English Interpretation Learning System Combining Cognitive Ability and Collaborative Filtering Algorithm

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