基于模糊算法的人工智能英语语音翻译系统应用研究

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS International Journal of Fuzzy Logic and Intelligent Systems Pub Date : 2021-03-29 DOI:10.3233/JIFS-189829
Erying Guo
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引用次数: 10

摘要

随着全球化的发展,人们对英语音频互动的需求越来越大。为了克服传统翻译方法在语法变量上存在语义歧义、量词错误、翻译精度低等缺点,提高英语翻译的质量和速度,获得更准确、更有速度保证的翻译,本研究提出了一种基于模糊算法的人工智能英语音频跨语言翻译系统。在本实验中,将采集到的模拟语音信号转换为数字语音信号,然后对语音特征进行建模和数字化,并对整套语音样本进行整合和修改,尽可能地消除噪声带来的干扰。之后将采集到的语音以文本格式存储,再对文本进行翻译,实现英语音频翻译。采用DNN-HMM语音识别模型和传统的GMM-HMM语音识别模型对原始语料库进行预处理,并对预处理后的语料库处理精度进行比较。然后,在第一类TSK和第二类TSK之间评价模糊算法的精度和利用率。对于语料库缺乏语言的语音合成,探索使用最少的训练数据来合成可接受的语音是有意义的。实验结果表明,模糊算法的准确率约为97.34%,利用率约为98.14%。类型1和类型2算法的准确率分别约为85.77%和76.87%,利用率分别约为83.25%和78.63%。基于模糊算法的人工智能英语语音跨语言翻译系统明显优于其他两种算法。
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Application research of artificial intelligence English audio translation system based on fuzzy algorithm
With the development of globalization, people’s demand for English audio interaction is increasing. In order to overcome the shortcomings of traditional translation methods in grammatical variables, such as semantic ambiguity, quantifier errors, low translation accuracy, improve the quality and speed of English translation, and get more accurate and speed guaranteed translation, this study proposes an artificial intelligence English audio translation cross language system based on fuzzy algorithm. In this experiment, the collected analog speech signal is converted into a digital speech signal, and then, the speech features are modeled and digitized, and the whole set of speech samples are integrated and modified to eliminate the interference caused by noise as far as possible. After that, the collected voice will be stored in the text format, and then the text will be translated to achieve English audio translation. The DNN-HMM speech recognition model and the traditional GMM-HMM speech recognition model are used to preprocess the original corpus, and the accuracy of the corpus processing is compared. After that, the accuracy and utilization of the fuzzy algorithm are evaluated between the first type TSK and the second type TSK. For speech synthesis in which the corpus lacks language, it is meaningful to explore the least amount of training data for the synthesis of acceptable speech. The experimental results show that the accuracy of the fuzzy algorithm is about 97.34%, and the utilization rate is about 98.14%. The accuracy rate of type 1 and type 2 algorithms are about 85.77% and 76.87% respectively, and the utilization rate is about 83.25% and 78.63% respectively. The fuzzy algorithm based artificial intelligence English audio translation cross language system is obviously better than the other two algorithms.
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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