Wu Xu, Zhang Qian
School of Art and Design, Tianjin University of Technology, Tianjin, China.
School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, China.
Front Psychol. 2022 May 4;13:882709. doi: 10.3389/fpsyg.2022.882709. eCollection 2022.
The rapid development of computer technology and artificial intelligence is affecting people's daily lives, where language is the most common way of communication in people's daily life. To apply the emotion information contained in voice signals to artificial intelligence products after analysis, this article proposes a design based on voice emotion recognition for aging intelligent home products with RBF. The authors first aimed at a smart home design, and based on the problem of weak adaptability and learning ability of the aging population, a speech emotion recognition method based on a hybrid model of Hidden Markov/Radial Basis Function Neural Network (HMM/RBF) is proposed. This method combines the strong dynamic timing modeling capabilities of the HMM model and the strong classification decision-making ability of the RBF model, and by combining the two models, the speech emotion recognition rate is greatly improved. Furthermore, by introducing the concept of the dynamic optimal learning rate, the convergence speed of the network is reduced to 40.25s and the operation efficiency is optimized. Matlab's simulation tests show that the recognition speed of the HMM/RBF hybrid model is 9.82-12.28% higher than that of the HMM model and the RBF model alone, confirming the accuracy and superiority of the algorithm and model.
计算机技术和人工智能的快速发展正在影响人们的日常生活，而语言是人们日常生活中最常见的交流方式。为了在分析后将语音信号中包含的情感信息应用于人工智能产品，本文提出了一种基于语音情感识别的RBF老年智能家用产品设计。作者首先针对智能家居设计，基于老年人群适应性和学习能力较弱的问题，提出了一种基于隐马尔可夫/径向基函数神经网络（HMM/RBF）混合模型的语音情感识别方法。该方法结合了HMM模型强大的动态时序建模能力和RBF模型强大的分类决策能力，通过将两种模型相结合，大大提高了语音情感识别率。此外，通过引入动态最优学习率的概念，将网络的收敛速度降低到40.25秒，优化了运行效率。Matlab的仿真测试表明，HMM/RBF混合模型的识别速度比单独的HMM模型和RBF模型高9.82-12.28%，证实了算法和模型的准确性和优越性。
