Nkululeko – An Open-Source Platform to Teach Machine Learning
In cooperation with the Technical University of Berlin, audEERING is developing an open-source platform to teach machine learning to interested laymen.
devAIce® Web API 4.1.0 Update
This latest release of devAIceⓇ Web API introduces updated dimensional and categorical emotion models in the Emotion (Large) module. In benchmarks, the new versions of these models are shown to be significantly more robust against background noises and different recording conditions than the previous models, all while keeping the computational complexity of the models unchanged.
devAIce® SDK 3.7.0 Update
Today, we are happy to announce the public release of devAIceⓇ SDK 3.7.0. This update comes with several noteworthy model updates for emotion and age recognition, the deprecation of the Sentiment module, as well as numerous other minor tweaks, improvements and fixes.
devAIce Web API 4.0.0 Update
We are proud to announce version 4.0.0 as a major update to devAIce Web API that is available to customers today. Most notably, this release introduces a modernized and simplified set of new API endpoints, all-new client libraries with support for more programming languages, OpenAPI compatibility, as well as an enhanced command-line interface tool. It also includes recent model updates and performance improvements from the latest devAIce SDK release, i.e. support for the Dominance emotion dimension and accuracy improvements of up to 15 percentage points.
devAIce® SDK 3.6.1 Update
The devAIce® team is proud to announce the availability of devAIce SDK 3.6.1 which comes with a number of major enhancements, exciting new functionality and smaller fixes since the last publicly announced version, 3.4.0. This blog post summarizes the most important changes that have been introduced in devAIce® SDK since then.
SHIFT – Cultural Heritage transformation project kicks off under Horizon Europe
SHIFT: MetamorphoSis of cultural Heritage Into augmented hypermedia assets For enhanced accessibiliTy and inclusion supports the adoption of digital transformation strategies and the uptake of tools within the creative and cultural industries (CCI), where progress has been lagging.
audEERING is now participating in the SHIFT project with the goal of synthesizing emotional speech descriptions of historical exhibits. This will change the way one experiences a historical monument, especially for visually impaired people.
Affective Avatars & The Voice AI Solution
We use avatars to show our identity or assume other identities, and want to make sure we express ourselves the way we want. The key factor in expression is emotion. Without recognizing emotions, we have no way of modifying a player’s avatar to express their expression and individuality.
With entertAIn play, recognizing emotion becomes possible.
Voice AI Perceives Emotions in Every World
Human interaction is based on a language, on a context, on a world knowledge that we share. As a Voice AI company, we know that emotion is the key factor. Emotional expression gets us moving, creates movement and a collective response. It is a key factor in society. It is the basis for all the decisions we make. In creating a virtual reality, new dimensions and augmented experiences, this key factor cannot be missing.
Closing the Valence Gap in Emotion Recognition
2021 has been an exciting year for our researches working on the recognition of emotions from speech. Benefiting from the recent advances in transformer-based architectures, we have for the first time built models that predict valence with a similar high precision as arousal.
New Updates on audEERING’s Core Technology: devAIce SDK/Web API 3.4.0
We are proudly announcing a new class of next-gen emotion models coming to devAIce with our latest 3.4.0 release of devAIce TM SDK/Web API.
Human in the Loop – How Do We Create AI?
Developing AI technology as we do at audEERING, we need to understand our human perception. Everyday perception is enabling us to realize the emotional state of our communication partner in different situations. In the process of Human Machine Learning we need to give the algorithm essential input. How do we at audEERING create AI?