EmotionML: Overview on IRs: Content

Emotion Markup Language 1

EmotionML: Overview on IRs: Content

Status of EmotionML SpecOverview potentially non-implemented featuresOverview Implementers

ALMA

C# library

EMLPy

EMO20Q

Gtrace

Mary TTS

Nviso

Speechalyzer

Wasabi

Emotion Markup Language 2

EmotionML: Overview on Implementation Reports

Status: Last call working draftCollecting Implementation Reports and answering public feedbackBy 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 featuresThere’s also a common journal publication planned under the lead of Edmon Begoli

Emotion Markup Language 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

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EmotionML: IR submitting institutions

Telekom Innovation Laboratories

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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

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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

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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.

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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

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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.

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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.

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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.

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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.

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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.