Automatic emotion recognition from DEMoS corpus by machine learning analysis of selected vocal features

Although Speech Emotion Recognition (SER) has become a major area of research in affective computing, the automatic identification of emotions in some specific languages, such as Italian, is still under-investigated. In this regard, we assess how different machine learning methods for SER can be applied in the identification of emotions in Italian language. In agreement with studies that criticize the use of acted emotions in SER, we considered DEMoS, a new database in Italian built through mood induction procedures. The corpus consists of 9365 spoken utterances produced by 68 Italian native speakers (23 females, 45 males) in a variety of emotional states. Experiments were carried out for female and male separately, considering for each a specific feature set. The two feature sets were selected by applying Correlation-based Feature Selection from the INTERSPEECH 2013 ComParE Challenge feature set. For the classification process, we used Support Vector Machine. Confirming previous work, our research outcomes show that the basic emotions anger and sadness are the best identified, while others more ambiguous, such as surprise, are worse. Our work shows that traditional machine learning methods for SER can be also applied in the recognition of an under-investigating language, such as Italian, obtaining competitive results.