Abstract
Receiving an accurate emotional response from robots has been a challenging task for researchers for the past few years. With the advancements in technology, robots like service robots interact with users of different cultural and lingual backgrounds. The traditional approach towards speech emotion recognition cannot be utilized to enable the robot and give an efficient and emotional response. The conventional approach towards speech emotion recognition uses the same corpus for both training and testing of classifiers to detect accurate emotions, but this approach cannot be generalized for multi-lingual environments, which is a requirement for robots used by people all across the globe. In this paper, a series of experiments are conducted to highlight an ensemble learning effect using a majority voting technique for cross-corpus, multi-lingual speech emotion recognition system. A comparison of the performance of an ensemble learning approach against traditional machine learning algorithms is performed. This study tests a classifier’s performance trained on one corpus with data from another corpus to evaluate its efficiency for multi-lingual emotion detection. According to experimental analysis, different classifiers give the highest accuracy for different corpora. Using an ensemble learning approach gives the benefit of combining all classifiers’ effect instead of choosing one classifier and compromising certain language corpus’s accuracy. Experiments show an increased accuracy of 13% for Urdu corpus, 8% for German corpus, 11% for Italian corpus, and 5% for English corpus from with-in corpus testing. For cross-corpus experiments, an improvement of 2% when training on Urdu data and testing on German data and 15% when training on Urdu data and testing on Italian data is achieved. An increase of 7% in accuracy is obtained when testing on Urdu data and training on German data, 3% when testing on Urdu data and training on Italian data, and 5% when testing on Urdu data and training on English data. Experiments prove that the ensemble learning approach gives promising results against other state-of-the-art techniques.
Highlights
Emotions help people communicate and understand others’ opinions by conveying feelings and giving feedback to people [46]
– Evaluate the effectiveness of the ensemble technique. – Present a comparative analysis of conventional machine learning techniques: decision tree (J48), random forest (RF), and sequential minimal optimization (SMO) with an ensemble of these machine learning algorithms using majority voting. – Ensemble learning approach effectively enhances the detection of emotion and achieves good accuracy on both with-in as well as cross-corpus data in comparison with conventional machine learning techniques
This study works on four corpora (SAVEE, URDU, EMO-DB, and EMOVO) that give a diversity of languages (English, Urdu, German, and Italian) to test for multi-lingual speech emotion recognition
Summary
Emotions help people communicate and understand others’ opinions by conveying feelings and giving feedback to people [46]. Ensemble learning helps to improve the performance of the machine learning models[17,29,33] This prompts for further exploration of different techniques that can be used to improve cross-corpus speech emotion recognition that will enable the deployment of speech emotion recognition systems in reallife applications. Different machine learning algorithms [32] have been used to accurately classify emotions with-in the same corpus, but when applied to cross-corpus, the performance has been average. Researchers propose a speech emotion recognition system for robots that uses a combination of different audio features to detect accurate emotion with-in a corpus as well as cross-corpus using the ensemble learning approach. – Ensemble learning approach effectively enhances the detection of emotion and achieves good accuracy on both with-in as well as cross-corpus data in comparison with conventional machine learning techniques. “Comparative analysis” presents comparative analysis and “Conclusion” concludes along with directions for future work
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