Purpose

Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

Design/methodology/approach

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Findings

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

Originality/value

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

中文翻译：

---

基于粒子群均值更新和鲸鱼优化的深度信念网络的新型语音情感识别模型

目的

如今，语音情感识别（SER）模型已成为人机交互以及语音处理等各个领域的主要研究主题。通常，它专注于利用机器学习模型来预测语音的确切情绪状态。先进的SER应用程序在情感计算和人机交互方面取得了成功，这已成为下一代计算机系统的主要组件。这是因为自然的人机界面可以授予自动服务规定，这需要更好地了解用户的情绪状态。

设计/方法/方法

本文实现了一个新的SER模型，该模型结合了性别和情感识别。提取某些特征并对其进行分类。为此，本文使用深度信念网络DBN模型。

发现

通过性能分析发现，与其他方法相比，该方法具有较高的准确率（最佳情况），比鲸鱼优化算法（WOA）高1.02％，比萤火虫（FF）高0.32％，优于粒子群优化（PSO）的23.45％和优于遗传算法（GA）的23.41％。在最坏的情况下，粒子群和鲸鱼优化（MUPW）在准确性方面的平均更新分别比WOA，FF，PSO和GA高出15.63％，15.98％，16.06％和16.03％。在平均情况下，MUPW的性能很高，与现有方法（如WOA，FF，PSO和GA）相比，分别提高了16.67％，10.38、22.30和22.47％。

创意/价值

本文提出了一种新的SER模型，该模型有助于性别和情感识别。为了进行分类，使用了DBN并使用了DBN的权重，这是首次使用MUPW算法来寻找DBN模型的最佳权重。