EmotionML: Overview on IRs: Content

1. EmotionML: Overview on IRs: Content • Status of EmotionML Spec • Overview potentially non-implemented features • Overview Implementers • ALMA • C# library • EMLPy • EMO20Q • Gtrace • Mary TTS • Nviso • Speechalyzer • Wasabi

2. EmotionML: Overview on Implementation Reports • Status: Last call working draft • Collecting Implementation Reports and answering public feedback • By now 9 IRs were submitted, one is announced (TU-Munich) but will come too late (end of year) • The core of the spec was implemented, but some elements are in danger (following slide) • We’re in the process of waiting for extensions to implement missing features • There’s also a common journal publication planned under the lead of Edmon Begoli

3. EmotionML: Features in danger • There’s two implementations, that support action-tendency and appraisal-set, but don’t check the names against the vocab, which is required. • Implementers promised to deliver till end of month • There’s only one implementation so far for the time-stuff taken from EMMA, i.e. start, end, duration, time-ref-uri, offset-to-start. • Because we declared these “at risk”, it should be ok to keep them in the spec. • There are two implementations that use “media-type” (416), but both don’t check if it’s a valid mime-type, which is an required assertion (417). • The implementers are notified and try t implement this, although it’s unclear which mime-type vocab to check

4. EmotionML: IR submitting institutions Telekom InnovationLaboratories

5. EmotionML IR: Alma • DFKI • http://www.dfki.de/~gebhard/alma/ • ALMA: A Layered Model of Affect. • is a computational model for the real-time simulation of three basic affect types • The ALMA system is an affect producing system. • It outputs EmotionML

6. EmotionML IR: C# Library • Univ. of Chemnitz • https://github.com/gfobe/EmotionML-Lib-CSharp • Master’s thesis at the Univ. of Chemnitz • Open source project at Github • Used in “Smiley ontology” • EmotionML C# library used to describe Emoticons in IRC chat protocol services

7. EmotionML IR: EMLPy • Oak Ridge National Laboratory/University of Tennessee • https://github.com/ebegoli/EMLPy • a Python based library for generation of EmotionML compliant documents. • Open source with Github • ultimately (and distantly) using EmotionML to help autistic children with alternative representations of emotional content in the material.

8. EmotionML IR: EMO20Q • USC-SAIL • http://sail.usc.edu/emo20q/ • a experimental framework for studying how people describe emotions in language • and how computers can simulate this type of verbal behavior. • Open source

9. EmotionML IR: Gtrace • Queen’s Univ. Belfast • http://mary.dfki.de/ • Software to trace emotional expression in videos. • The system currently implements tracing for category and dimensional descriptors.

10. EmotionML IR: Mary TTS • DFKI • http://mary.dfki.de/ • MARY is an open-source, multilingual Text-to-Speech Synthesis platform that includes modules for expressive speech synthesis. • Particularly the support for both categorical and dimensional representations of emotions is important to its expressive speech synthesis system MARY TTS.

11. EmotionML IR: NViso • nViso • http://nviso.ch • nViso 3D Facial Imaging API is an online service for recognition emotions depicted through facial expressions in still images and videos. • The focus of this implementation of EmotionML is on using the media type and URI time for video.

12. EmotionML IR: Speechalyzer • Deutsche Telekom Laboratories • https://github.com/dtag-dbu/speechalyzer • Analysis / annotation / transcription tool “Speechalyzer” • Open Source project in Github • Can be used to rapidly judge large number of audio files emotionally. • Automatic classification integrated. • Uses EmotionML as exchange format.

13. EmotionML IR: Wasabi • Univ. of Freiburg • https://github.com/CBA2011 • WASABI architecture for affect simulation • Applied to the articulated communicator Max of the Univ. of Bielefeld • To let the WASABI architecture seamlessly interface with other software modules, a standard interface such as EmotionML is indispensable.