# Recognition of Cross-Language Acoustic Emotional Valence Using Stacked Ensemble Learning
## 1 Introduction
In addition, cross-language communication is beneficial in most customer call centers in African countries where multiple languages are official languages approved by the government. For example, in Zimbabwe, there are 16 official languages, and multilingual support in Shona, Ndebele, English, and Tonga are frequently used at call centers for communication. Such an environment demands the development of a versatile cross-language VSER model instead of a separate speech recognition model for each official language. Hence, there is the need to develop speech emotion models using several corpora with different languages, cultures, and recording environments. This novel approach is widely referred to as cross-language speech emotion recognition, which is another focus of this study. Cross-language speech emotion recognition is a difficult task because of the sparseness and intrinsic dissimilarities among speech emotion corpora [25]. Most of the existing corpora consist of different languages, accents, and speakers from different culture and heritage [26]. Speech emotions are generally recorded under varying conditions and environments that can be noisy or proliferated with undesirable artifacts [24]. The quality of equipment used for recording the vocal utterances may be different, which can inhibit speech recognition performance [27]. Differential in the gender bigotry and age chauvinism of speakers can also impede speech recognition performance [28]. There are three standard types of emotion corpora that are frequently exploited for speech recognition experiments, which are acted, elicited, and natural [29]. The techniques used in creating speech emotion recognition corpora can significantly affect the output of the recognition systems [23]. These are the critical factors that can affect the performance of a speech recognition system when tested on different corpora. Consequently, there is a necessity to investigate the most discriminative speech features and effective recognition methods that can help recognize speech emotions in multilingual settings. The RALOG ensemble learning algorithm proposed in this paper--a combination of random decision forest, AdaBoost, logistic regression, and gradient boosting machine--can effectively recognize multilingual positive and negative emotional valences. The choice of an ensemble algorithm in this study was inspired by the success story of various practical applications of ensembles such as lung cancer prediction [30], gender voice identification [31], hyperspectral image classification [32], and product image classification [33]. Therefore, this research work makes the following distinctive contributions to the scientific body of knowledge. * The selection of highly discriminating features from an existing set of acoustic features using a random forest recursive feature elimination (RF-RFE) algorithm to seamlessly recognize positive and negative valence emotions in a cross-language environment is a unique contribution of this study. Most of the existing methods reported in the literature hardly use feature selection for cross-language speech recognition, yet it is essential to identify the features that can easily distinguish valence emotions across different languages and accents. * The development of a RALOG-staked ensemble learning algorithm that can effectively recognize positive and negative valence emotions in human spoken utterances with high precision, recall, F1 score, and accuracy values is an important contribution of this study. Moreover, the training time of the algorithm was greatly reduced through feature scaling to improve the efficiency of the algorithm. * The experimental comparison of various benchmarked ensemble learning algorithms to test the effectiveness of the proposed RALOG algorithm based on the selected acoustic features is an essential contribution of this research work. The remainder of this paper is concisely summarized as follows. The related literature is discussed in Section 2. The study materials and methods are presented in Section 3.