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Found 3 papers, 1 papers with code
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks
The current state-of-the-art methods proposed for these tasks are ensembles based on deep neural networks like ResNets in conjunction with feature engineering.
Data augmentation using prosody and false starts to recognize non-native children's speech
Firstly, we apply the prosody-based data augmentation to supplement the audio data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge
On ComParE 2020 tasks, we investigate applying an ensemble of E2E models for robust performance and developing task-specific modifications for each task.
