EmoVoice, the real-time speech emotion recognition component, provides a framework for building an emotion classifier and for recognizing emotions in real-time. It extracts from speech signals a vector of emotion-relevant acoustic features (e.g. derived from pitch, energy, voice quality, pauses, spectral information) and then uses a statistical classifier, trained by examples, to assign emotion labels. Thus, the component relies on acoustic information only, word meanings are not used.

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Availability of the component: Reference contacts are Thurid Vogt and Elisabeth André of University of Augsburg.
Notes: EmoVoice as toolbox for SSI is now available freely for download under the GNU Public Licences. All information on how to download EmoVoice are provided here  A registration prior to the download is necessary. Combination of EmoVoice with SSI, includes modules for database creation; feature extraction + classifier building and testing; online recognition. Currently, there is no classifier or training data provided with EmoVoice: to create an own classifier based on self-recorded emotional speech corpus the SSI/ModelUI is also needed. The SSI Framework is a prerequisite to change or to recompile EmoVoice. Both the framework and ModelUI can be obtained from here.

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Recently integrated into Smart Sensor Integration CALLAS toolkit, the component comes with a graphical user interface to record an emotional database reference for training a classifier tailored to a specific task: see also dedicated page

Besides functional tests, extensive experimentation in CALLAS is done in Scientific Showcases:

• the e-Tree : in this Augmented Reality art installation the component is used to derive emotional valence and arousal recognised from voice.
• the CommonTouch : the component is trained for affective input with a small group of users interacting and speaking in native French language as a preparation for an exhibition in Paris.

The component is also used in Proof-of-Concepts applications and other demonstrators:

• for Interactive Digital Theater: in the Galaxies the component detects neutral, positive and negative emotions of people to influence the aestethic of the galaxies projected on the stage.
• for edutainment: in the Musickiosk the component is used in conjunction with a customised dictionary (Italian, French and English) for the words selected for the kiosk

• EmoVoice | A framework for online recognition of emotions from voice: Abstract
• Automatic Recognition of Emotions from Speech: A Review of the Literature and Recommendations for Practical Realisation: Abstract
• A systematic  comparison of different HMM designs for emotion recongnition from acted and spontaneous speech: Abstract
• Exploring the benefits of discretization of acoustic features for speech emotion recognition: Abstract