# Recognition of Cross-Language Acoustic Emotional Valence Using Stacked Ensemble Learning
## Materials and Methods
### Speech Emotion Corpora  
The speech emotion corpora of the present study are EMO-DB, SAVEE, RAVDESS, EMOVO, and CREMA-D. The EMO-DB was developed using ten professional German language speaking actors who were five males and five females. Their German spoken utterances were recorded to express seven emotional states of happiness, boredom, disgust, sadness, anger, neutral, and fear. The SAVEE corpus consists of 480 utterances spoken in the British English accent ([PERSON] et al., 2017). The corpus was developed using 4 male professional actors expressing seven emotional states of angry, disgust, fear, happy, neutral, sad, and surprise. The RAVDESS is a gender-balanced emotion corpus that consists of 1440 vocal utterances that were recorded from professional actors involving twelve males and twelve females [43]. The corpus was developed with the North American English accent, which is different from the one used in SAVEE. Moreover, RAVDESS is a multiclass emotion corpus that consists of eight emotional states of angry, calm, disgust, fear, happy, neutral, sad, and surprise. The EMOVO corpus is an Italian emotional speech database that consists of utterances spoken in the Italian language [44]. The corpus was developed using six professional actors involving 3 males and 3 females who simulated seven emotional states of disgust, fear, anger, joy, surprise, sadness, and neutral. The CREMA-D corpus constitutes 6786 audio-visual clips recorded from 91 professional actors involving 48 males and 43 females from diverse races and ethnic backgrounds, including African, American, Asian, Caucasian, Hispanic, and Unspecified [45].  
In this study, only the audio files of five speech emotion databases were fused to create a cross-language experimentation corpus as inspired by the previous studies [25; 40]. The databases were specifically chosen because they consist of vocal utterances spoken in different languages and varying accents. The cross-language corpus was designed using eight speech emotional states of angry, happy, neutral, calm, sad, surprised, fear, and disgust. These emotions were mapped to positive and negative valences, as illustrated in Table 2[22; 41; 25]. The mapping of speech emotions to emotional valences has provided a seamless mechanism to unify different corpora with varying emotional states for cross-language emotion recognition experimentation. The distribution of utterances across the five corpora indicates a total of 10,094 adult utterances for the experimental dataset of this study. The distribution by gender categorization is imbalanced because about 54% of the total utterances constituted male utterances, while about 46% are female utterances. The SAVEE corpus contains male utterances, and the CREMA-D contains more male utterances than female utterances, which have made the experimental dataset imbalanced. The training and testing datasets of utterances respectively constituted 80% and 20% of the total utterances.  
The resulting cross-language corpus has produced 6641 sample files of negative valences, and 453 sample files present positive valences. It is important to observe that the dominance of negative valence emotions over positive valence emotions did not bias the prediction results toward the negative valence emotions. Figure 1 shows the distribution of both the negative valence instances (unsatisfied class) and positive valence instances (satisfied class) that are presented in the cross-language corpus of this study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
\begin{tabular}{c} Neutral, \\ Happiness, \\ Surprise \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mappings of adult speech emotions into positive and negative valence emotions.