.png){kind=icon}
返回
Enhancing human computer interaction with coot optimization and deep learning for multi language identification
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-024-74327-2.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Human-Computer Interaction (HCI) is a multidisciplinary field focused on designing and utilizing computer technology, underlining the interaction interface between computers and humans. HCI aims to generate systems that allow consumers to relate to computers effectively, efficiently, and pleasantly. Multiple Spoken Language Identification (SLI) for HCI (MSLI for HCI) denotes the ability of a computer system to recognize and distinguish various spoken languages to enable more complete and handy interactions among consumers and technology. SLI utilizing deep learning (DL) involves using artificial neural networks (ANNs), a subset of DL models, to automatically detect and recognize the language spoken in an audio signal. DL techniques, particularly neural networks (NNs), have succeeded in various pattern detection tasks, including speech and language processing. This paper develops a novel Coot Optimizer Algorithm with a DL-Driven Multiple SLI and Detection (COADL-MSLID) technique for HCI applications. The COADL-MSLID approach aims to detect multiple spoken languages from the input audio regardless of gender, speaking style, and age. In the COADL-MSLID technique, the audio files are transformed into spectrogram images as a primary step. Besides, the COADL-MSLID technique employs the SqueezeNet model to produce feature vectors, and the COA is applied to the hyperparameter range of the SqueezeNet method. The COADL-MSLID technique exploits the SLID process's convolutional autoencoder (CAE) model. To underline the importance of the COADL-MSLID technique, a series of experiments were conducted on the benchmark dataset. The experimentation validation of the COADL-MSLID technique exhibits a greater accuracy result of 98.33% over other techniques.
Keyword:
Spoken language identification
Human-computer interface
Coot optimization algorithm
SqueezeNet
Deep learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.0W
被引数:
83.5W
机构
引用论文
Diabetes, Cardiorenal, and Metabolic Multispecialty Practice Recommendations and Early Intensive Management of Cardio-Renal-Metabolic Disease糖尿病、心肾和代谢多专科实践建议及心-肾-代谢疾病的早期强化管理
PREAI
分享收藏PI3‐kinase pathway biomarkers in oral cancer and tumor immune cellsPI3‐kinase通路生物标志物在口腔癌及肿瘤免疫细胞中的作用OAAI
OAAI分享收藏分享收藏The unexpected effect of cyclosporin A on CD56+CD16− and CD56+CD16+ natural killer cell subpopulations.png)
Blood
OAAI
BloodIF0
OAAI分享收藏Current status and trends of technology, methods, and applications of Human-Computer Intelligent Interaction (HCII): A bibliometric research人机智能交互 (HCII) 的技术、方法和应用现状与趋势: 文献计量研究
PREAI
分享收藏
查看更多内容