# Recognition of Cross-Language Acoustic Emotional Valence Using Stacked Ensemble Learning
## 2 Related Literature  
Developing cross-language speech emotion recognition systems has been a hot research agenda in the field of human-machine interaction over the past few years. Researchers in the speech recognition area have proposed different methods, including feature normalization [34] and decision fusion [35] to improve the performance of speech emotion recognition systems. However, many of the research studies done in speech emotion recognition have been mainly focused on single-language speech recognition systems. Relatively very few studies have been done in cross-language speech emotion recognition systems that can be useful in a language immersion environment with the goal of constructing an intelligent machine that can understand and speak multiple human languages. The authors in [36] used a methodology based on the Geneva minimalistic acoustic parameter set (eGeMAPS), deep belief network (DBN)-based transfer learning, and leave-one-out (LOO) to improve the performance of a speech recognition system for the case of a cross-language application. They trained their model using the LOO technique with interactive emotional dyadic motion capture (IEMOCAP), EMO-DB, Italian emotional speech database (EMOVO), and SAVEE. They used the Friedrich Alexander Universitat artificial intelligence bot (FAU-Aibo) emotion corpus to test their model that achieved an overall accuracy of 80% [22]. [PERSON] et al. [25] used the EMOVO, Urdu speech dataset, SAVEE, and EMO-DB corpora of utterances spoken in Italian, Urdu, English, and German languages, respectively to develop a cross-language speech emotion recognition model. They used eGeMAPS with support vector machine (SVM) to classify the resulting emotions into positive and negative valences according to the emotion mapping shown in Table 1. Their cross-language valence speech classification model achieved an overall accuracy of 70.98%. [PERSON] et al. [37] proposed a novel triple attentive asymmetric CNN model to recognize emotion using the cross-language approach. Their model achieved an accuracy score of 73.11% after training on IEMOCAP and tested on the FAU Aibo corpus. [PERSON] et al. [38] achieved an overall accuracy of 56.5% using clean spectrograms with deep stride convolutional neural network (DSCNN) learning algorithm. They used a Ryerson audio-visual database of emotional speech and song (RAVDESS) and IEMOCAP speech emotion corpora to train and test their proposed model across four emotional states of anger, happy, neutral, and sad. The domain-adaptive least squares regression (DALSR) with SVM learning algorithm was presented and tested on eNTERFACE audio-visual emotion dataset and EMO-DB [39]. The study was done across six emotional states of angry, disgust, fear, happy, sad, and surprise to achieve an unweighted average accuracy (UAR) of 52.27%. [PERSON] et al. [40] combined an IS16 set of prosodic features with a speech modulation spectral feature (MSF) to develop a cross-language speech emotion recognition model [40]. They used the logistic model trees (LMT) learning algorithm to recognize four emotional states of neutral, happy, angry, and sad. The training and testing were done using the Fujitsu, EMO-DB and Chinese emotional database (CASIA) speech emotion corpora to achieve an F-Measure score of 82.63%. Speech emotion corpora of RAVDESS,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
IEMOCAP, EMO-DB, Italian emotional speech database (EMOVO), SAVEE, and emotional prosody speech and transcript (EPST) were used to train and test a cross-language speech emotion recognition model. The emotional states in these corpora were divided into three sentiment groups of positive, negative, and neutral. [PERSON] et al. (2018) used an INTERSPEECH 2009 emotion challenge baseline feature set and adaptive denoising autoencoder (A-DAE) to develop a cross-language speech emotion recognition model.