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.

在过去的几年中，从机器人接收准确的情绪反应一直是研究人员的一项艰巨任务。随着技术的进步，诸如服务机器人之类的机器人会与具有不同文化和语言背景的用户互动。传统的语音情感识别方法不能用于使机器人动作并给出有效的情感响应。传统的语音情感识别方法在训练和测试分类器时都使用相同的语料库，以检测准确的情感，但是这种方法不能推广到多语言环境，这是全球人们所使用的机器人的要求。在本文中，我们进行了一系列实验，以针对跨语料库使用多数投票技术来突出整体学习效果，多语言语音情感识别系统。进行了整体学习方法与传统机器学习算法的性能比较。这项研究测试了一个语料库上训练的分类器性能，并使用了另一个语料库中的数据，以评估其用于多语言情感检测的效率。根据实验分析，不同的分类器为不同的语料库提供最高的准确性。使用整体学习方法的好处是可以组合所有分类器的效果，而不是选择一个分类器，并且会损害某些语言语料库的准确性。实验显示，通过内置语料库测试，Urdu语料库的准确性提高了13％，德语语料库的准确性提高了8％，意大利语料库的准确性提高了11％，英语语料库的准确性提高了5％。对于跨主体实验，对乌尔都语数据进行培训和对德国数据进行测试时，可提高2％；对乌尔都语数据进行培训和对意大利数据进行测试时，可提高15％。对Urdu数据进行测试和对德国数据进行培训时，准确性提高了7％；对Urdu数据进行测试和对意大利数据进行培训则提高了3％；对Urdu数据进行测试和对英语数据进行培训时，提高了5％。实验证明，与其他最新技术相比，集成学习方法可提供令人鼓舞的结果。