School of Foreign Languages, Xidian University, Xi'an, Shanxi 710071, China.
Comput Intell Neurosci. 2022 Mar 27;2022:8469945. doi: 10.1155/2022/8469945. eCollection 2022.
With the in-depth promotion of the national strategy for the integration of artificial intelligence technology and entity development, speech recognition processing technology, as an important medium of human-computer interaction, has received extensive attention and motivated research in industry and academia. However, the existing accurate speech recognition products are based on massive data platform, which has the problems of slow response and security risk, which makes it difficult for the existing speech recognition products to meet the application requirements for timely translation of speech with high response time and network security requirements under the condition of network instability and insecurity. Based on this, this paper studies the analysis model of oral English evaluation algorithm based on Internet of things intelligent algorithm in speech recognition technology. Firstly, based on the automatic machine learning and lightweight learning strategy, a lightweight technology of automatic speech recognition depth neural network adapted to the edge computing power is proposed. Secondly, the quantitative evaluation of Internet of things intelligent classification algorithm and big data analysis in this system is described. In the evaluation, the evaluation method of oral English characteristics is adopted. At the same time, the Internet of things intelligent classification algorithm and big data analysis strategy are used to evaluate the accuracy of oral English. Finally, the experimental results show that the oral English feature recognition system based on Internet of things intelligent classification algorithm and big data analysis has the advantages of good reliability, high intelligence, and strong ability to resist subjective factors, which proves the advantages of Internet of things intelligent classification algorithm and big data analysis in English feature recognition.
随着国家人工智能技术与实体发展融合战略的深入推进，语音识别处理技术作为人机交互的重要媒介，在工业界和学术界受到了广泛关注和积极研究。然而，现有的准确语音识别产品是基于大数据平台的，存在响应慢和安全风险的问题，这使得现有的语音识别产品难以满足在网络不稳定和不安全的情况下对具有高响应时间和网络安全要求的语音进行及时翻译的应用要求。基于此，本文研究了基于物联网智能算法的语音识别技术中英语口语评估算法的分析模型。首先，基于自动机器学习和轻量级学习策略，提出了一种适用于边缘计算能力的自动语音识别深度神经网络的轻量级技术。其次，描述了本系统中物联网智能分类算法和大数据分析的定量评估。在评估中，采用英语口语特征的评估方法。同时，利用物联网智能分类算法和大数据分析策略对英语口语的准确性进行评估。最后，实验结果表明，基于物联网智能分类算法和大数据分析的英语口语特征识别系统具有良好的可靠性、较高的智能性和较强的抗主观因素能力，证明了物联网智能分类算法和大数据分析在英语口语特征识别中的优势。
