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Emotion recognition or affect detection from speech is an old and challenging problem in the field of artificial intelligence. Many significant research works have been done on emotion recognition. In this paper, the recent works on affect detection using speech and different issues related to affect detection has been presented. The primary challenges of emotion recognition are choosing the emotion recognition corpora (speech database), identification of different features related to speech and an appropriate choice of a classification model. Different types of methods to collect emotional speech data and issues related to them are covered by this presentation along with the previous works review. Literature survey on different features used for recognizing emotion from human speech has been discussed. The significance of various classification models has been presented along with some recent research works review. A detailed description of a prime feature extraction technique named Mel Frequency Cepstral Coefficient (MFCC) and brief description of the working principle of some classification models are also discussed here. In this paper terms like affect detection and emotion recognition are used interchangeably.
Basu et al. (Wed,) studied this question.