12 citations · 19 across the 7 of their papers we have counts for
7 papers
Uncertainty-Based Ensemble Learning For Speech Classification
Bagus Tris Atmaja, Felix Burkhardt
Speech classification has attracted increasing attention due to its wide applications, particularly in classifying physical and mental states. However, these tasks are challenging…
Ensembling Multilingual Pre-Trained Models for Predicting Multi-Label Regression Emotion Share from Speech
Bagus Tris Atmaja, Akira Sasou
Speech emotion recognition has evolved from research to practical applications. Previous studies of emotion recognition from speech have focused on developing models on certain dat…
Effect of different splitting criteria on the performance of speech emotion recognition
Bagus Tris Atmaja, Akira Sasou
Traditional speech emotion recognition (SER) evaluations have been performed merely on a speaker-independent condition; some of them even did not evaluate their result on this cond…
Predicting Affective Vocal Bursts with Finetuned wav2vec 2.0
Bagus Tris Atmaja, Akira Sasou
The studies of predicting affective states from human voices have relied heavily on speech. This study, indeed, explores the recognition of humans' affective state from their vocal…
Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation
Bagus Tris Atmaja, Zanjabila, Suyanto +1
This paper addresses issues on cough-based COVID-19 detection. We propose a cross-dataset transfer learning approach to improve the performance of COVID-19 detection by incorporati…
Comparing Hysteresis Comparator and RMS Threshold Methods for Automatic Single Cough Segmentations
Bagus Tris Atmaja, Zanjabila, Suyanto +1
Research on diagnosing diseases based on voice signals currently are rapidly increasing, including cough-related diseases. When training the cough sound signals into deep learning…
Jointly Predicting Emotion, Age, and Country Using Pre-Trained Acoustic Embedding
Bagus Tris Atmaja, Zanjabila, Akira Sasou
In this paper, we demonstrated the benefit of using pre-trained model to extract acoustic embedding to jointly predict (multitask learning) three tasks: emotion, age, and native co…