# Recognition of Cross-Language Acoustic Emotional Valence Using Stacked Ensemble Learning
## 1 Introduction  
The advent of machine learning methods has brought about groundbreaking research and ubiquitous applications of different algorithms to solve challenging problems facing humans. Nowadays, we live in a world where the idea of detecting skin melanoma [1] and plant diseases [2] is made possible because of the innovation of machine learning. The field of communication network has not been spared in the pervasive adoption of intelligent systems for supporting human decision-making process. The necessity to develop intelligent robotic systems has triggered innovative research in many fields such as the healthcare, industry automation [3], human-computer interaction [4], and many other practical application areas. Embedding emotional intelligence in business units such as customer call centers is providing a better alternative of measuring customer satisfaction and evaluation of agents [5]. The development of a model that can recognize customer satisfaction requires a blend of different languages and accents. Developing systems that are emotionally intelligent requires an appropriate model to represent emotions [6]. The categorical approach and dimensional approach are two possible ways of modeling human emotions [7]. Categorical models divide emotions into several categories such as sadness, surprise, fear, and so on [7]. The dimensional approach presents speech emotions in different dimensions such as the three dimensions of arousal, dominance, and valence [8]. Arousal describes how excited or apathetic an individual gets when a particular event triggers [9]. Dominance describes submission, while polarity-based valence speaks to the positivity and negativity of human emotions [10]. This research focuses on valence emotion, which is one of the most significant scientific concepts that lies at the core of emotional experience. Emotional valence expresses the pleasantness or hedonic value of emotional states and classifies them appositely as positive or negative [11]. In fact, valence can be used to evaluate user experience in human-computer interactivity [12]. Moreover, it can be used to determine the satisfaction, contentment, and approval of clients in a customer call center environment [13]. A customer may register a grievance at a call center showing discontent or disenchantment with regard to the service offered but may end up expressing feelings of joy or contentment after receiving favorable feedback from the call center agent. A call center agent might be efficient and soft spoken and thereby fulfilling the basic functional needs of the user. However, if it creates a bad experience, the users obviously would not like it, which can create emotional acrimony or a barrier. Therefore, this research work is focused on valence speech emotion recognition (VSER) with the purpose of extracting discriminative acoustic features from speech signals to improve recognition performance [14]. The application of such a recognition system will not only enable us to comprehend the meaning of the messages being conveyed, but to also know how messages are conveyed [15]. [PERSON] et al. [16] have suggested unequivocally that for listeners to get a deeper comprehension of messages communicated, there is the need to understand the emotional states of the speakers involved in the communication. Some researchers have fused facial and speech features to improve the recognition of depression in an individual [17; 18]. However, many of the previous studies done in VSER have been conducted using monolingual emotion corpus such as Surrey audio-visual expressed emotion (SAVEE) [19] and the Berlin database of emotional speech (EMO-DB) [20]. Models developed using monolingual corpora have performed well when tested on such architypes of corpora, but they often perform poorly on multilingual emotion corpora [21; 22]. Moreover, research has shown that speech emotion models perform better when applied under the conditions that are analogous to the original environment and circumstance [23; 24].