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Speech emotion recognition from voice messages recorded in the wild
The pre-trained Unispeech-L model and its combination with eGeMAPS achieved the highest results, with 61. 64% and 55. 57% Unweighted Accuracy (UA) for 3-class valence and arousal prediction respectively, a 10% improvement over baseline models.
Emotional Voice Messages (EMOVOME) database: emotion recognition in spontaneous voice messages
For speech, we used the standard eGeMAPS feature set and support vector machines, obtaining 49. 27% and 44. 71% unweighted accuracy for valence and arousal respectively.
Comparing Feature Engineering and End-to-End Deep Learning for Autism Spectrum Disorder Assessment based on Fullbody-Tracking
Our aim is to assess both approaches across various kinematic tasks to measure the efficacy of commonly used features in ASD assessment, while comparing them to end-to-end models.
Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses
Alzheimer's Disease (AD) is the world's leading neurodegenerative disease, which often results in communication difficulties.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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