In recent years, speech emotion recognition (SER) has been used in wide ranging applications, from healthcare to the commercial sector. In addition to signal processing approaches, methods for SER now also use deep learning techniques. However, generalizing over languages, corpora and recording conditions is still an open challenge in the field. Furthermore, due to the black-box nature of deep learning algorithms, a newer challenge is the lack of interpretation and transparency in the models and the decision making process. This is critical when the SER systems are deployed in applications that influence human lives. In this work we address this gap by providing an in-depth analysis of the decision making process of the proposed SER system. Towards that end, we present low-complexity SER based on undercomplete- and denoising- autoencoders that achieve an average classification accuracy of over 55% for four-class emotion classification. Following this, we investigate the clustering of emotions in the latent space to understand the influence of the corpora on the model behavior and to obtain a physical interpretation of the latent embedding. Lastly, we explore the role of each input feature towards the performance of the SER.
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