Abstract

In recent times, the rise of several multimodal (audio, video, etc.) content-sharing sites like Soundcloud and Dubsmash have made development of sentiment analytical techniques for these imperative. Particularly, there is much to explore when it comes to audio data, which has proliferated rapidly. Of all the various aspects of audio sentiment studies, emotion recognition in speech signals has gained momentum and attention in recent times. Recognizing specific emotions inherent in spoken language could go a long way in healthcare, information sciences, human–computer interaction, etc. This chapter examines the process of delineating sentiments from speech, and the impact of various deep learning techniques on the same. Factors like extracting relevant features and the performances of several deep learning architectures on such datasets are analyzed. Performances using various classical and deep learning approaches are presented as well. Finally, some conclusions and suggestions on the way forward are discussed.