Recognition of Emotion from Speech: A Review

S. Ramakrishnan

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Emotional speech recognition is an area of great interest for human-computer interaction. The system must be able to recognize the user’s emotion and perform the actions accordingly. It is essential to have a framework that includes various modules performing actions like speech to text conversion, feature extraction, feature selection and classification of those features to identify the emotions. The classifications of features involve the training of various emotional models to perform the classification appropriately. Another important aspect to be considered in emotional speech recognition is the database used for training the models. Then the features selected to be classified must be salient to identify the emotions correctly. The integration of all the above modules provides us with an application that can recognize the emotions of the user and give it as input to the system to respond appropriately.

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References

2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2010EEG-Based Emotion Recognition in Music Listening 2009Emotion Recognition From EEG Using Higher Order Crossings 2003Emotional speech: Towards a new generation of databases 2004Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture 2009Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection 2009Acoustic emotion recognition: A benchmark comparison of performances 2007Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech

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