most citedOn the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era

9 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Depression Diagnosis and Forecast based on Mobile Phone Sensor Data

Xiangheng He, Andreas Triantafyllopoulos, Alexander Kathan +9

Previous studies have shown the correlation between sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires…

cs.LG2022

Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting

Alexander Kathan, Andreas Triantafyllopoulos, Xiangheng He +9

Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A comm…

cs.SD2022

Predicting Sex and Stroke Success -- Computer-aided Player Grunt Analysis in Tennis Matches

Lukas Stappen, Manuel Milling, Valentin Munst +2

Professional athletes increasingly use automated analysis of meta- and signal data to improve their training and game performance. As in other related human-to-human research field…

cs.LG2021

Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations

Andreas Triantafyllopoulos, Manuel Milling, Konstantinos Drossos +1

Underspecification and fairness in machine learning (ML) applications have recently become two prominent issues in the ML community. Acoustic scene classification (ASC) application…

cs.SD20219 cited

On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era

Shahin Amiriparian, Artem Sokolov, Ilhan Aslan +9

Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challengi…