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通过coot优化和深度学习来增强人与计算机的交互,以实现多语言识别

Elvir Akhmetshin^1,2, Galina Meshkova³, Maria Mikhailova⁴

¹Candidate of Economic Sciences, Department of Economics and Management, Kazan Federal University, Elabuga Institute of KFU, Elabuga, 423604, Russia.

Scientific reports

|October 3, 2024

概括

本研究介绍了一种新的Coot优化算法与深度学习用于多种口语识别 (MSLI) 在人机交互 (HCI). COADL-MSLID技术在检测增强的HCI应用程序的多种语言方面达到98.33%的准确性.

科学领域:

计算机科学计算机科学
人工智能的人工智能
人与计算机的交互

背景情况:

人与计算机交互 (HCI) 专注于设计有效和用户友好的计算机系统.
多种口语识别 (MSLI) 对于无的HCI至关重要,使系统能够识别多种语言.
深度学习 (DL) 技术,特别是神经网络,在语音和语言处理任务中显示出很大的前景.

研究的目的:

在人机交互 (HCI) 应用中开发一种用于多种口语识别 (MSLI) 的新技术.
提高语言检测系统的准确性和稳定性,无论说话者的特点如何.
为高级HCI引入Coot优化算法与DL驱动多重SLI和检测 (COADL-MSLID).

主要方法:

音频文件被转换成光谱图像进行分析.
使用SqueezeNet模型进行特征向量提取.
库特优化算法 (COA) 为SqueezeNet优化了超参数,并使用卷积自动编码器 (CAE) 进行口语识别 (SLID).

主要成果:

拟议的COADL-MSLID技术在检测多种口语方面表现出很高的性能.
在基准数据集上的实验验证结果准确率为98.33%.
该方法有效地识别了不同性别,说话风格和年龄的语言.

结论:

在HCI应用中,COADL-MSLID技术为MSLI提供了显著的进步.
整合COA和DL模型为口语检测提供了强大而准确的解决方案.
这种方法通过改进语言识别来提高人机交互的自然性和效率.

关键词:

库特优化算法 Coot优化算法深度学习是一种深度学习.人与计算机的接口.口语识别口语的识别方法这就是SqueezeNet.

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