Press Release: audEERING and iMotions collaborate to refine human behavioral research

iMotions multimodal platform showing audEERING's voice analysis powered by devAIce®

audEERING, the German market leader for AI-based audio analysis, is cooperating with iMotions, the world’s leading provider of software for interdisciplinary research into human behavior, to expand measurement and analysis areas in human science research. With the integrated voice AI component, the multimodal software suite takes on another dimension that provides insights into human behavior.

devAIce® SDK 3.8 and 3.9 Updates – New Powerful Module

devAIce SDK 3.7.0 Update Header presenting devices like smartwatch, headphones and VR glasses

We are pleased to unveil the latest updates in devAIce® SDK 3.8 and 3.9, bringing substantial improvements and new features to boost your development experience. In this blog post, we will provide a comprehensive overview of the noteworthy enhancements introduced in these releases. This includes the introduction to our new module, which can be used to analyze audio quality.

Robots Learn Empathy 

Hanson Robotics in cooperation with audEERING - leading innovator in Voice AI

audEERING and Hanson Robotics develop robots with the highest social competence for everyday use. One of the world’s leading robot manufacturers Hanson Robotics integrates automated emotion recognition devAIce from German AI company audEERING. Highest level of human-machine interaction opens up new chances for the use of robots on the job, for example in caregiving. Robots recognize the emotions of their counterparts by voice and increase their social competence as a result.

devAIce® Web API 4.1.0 Update

Web API Update Header presenting a Smartphone and Laptop with download symbol 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

devAIce SDK 3.7.0 Update Header presenting devices like smartwatch, headphones and VR glasses

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

devAIce Web API with a updated 4.0.0 Version including modernized and simpllified set of new API endpoints

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

devAice SDK 3.6.1 Update in a Emotion dimensions cube: valence, arousal, dominance

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.

Closing the Valence Gap in Emotion Recognition

blog gap between the valence gap in emotion regognition from voice. Finally closing the challgenge in speech emotion recogniton

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.