Search Results for author:
Found 8 papers, 6 papers with code
Ensembling Multilingual Pre-Trained Models for Predicting Multi-Label Regression Emotion Share from Speech
A Spearman rank correlation coefficient of 0. 537 is reported for the test set, while for the development set, the score is 0. 524.
Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst Tasks
The search for emotional biomarkers within the human voice is a challenging research area.
Ranked #1 on
Vocal Bursts Type Prediction
on HUME-VB
Automatic Speech Recognition
Cultural Vocal Bursts Intensity Prediction
+6
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts Tasks
Understanding humans’ emotions is a challenge for computers.
Effect of different splitting criteria on the performance of speech emotion recognition
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 condition.
Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation
This paper addresses issues on cough-based COVID-19 detection.
Comparing Hysteresis Comparator and RMS Threshold Methods for Automatic Single Cough Segmentations
The results by two automatic segmentation methods obtained precisions of 73% and 70% compared to 49% by manual segmentation.
Predicting Affective Vocal Bursts with Finetuned wav2vec 2.0
The studies of predicting affective states from human voices have relied heavily on speech.
Cultural Vocal Bursts Intensity Prediction
Speech Recognition
Jointly Predicting Emotion, Age, and Country Using Pre-Trained Acoustic Embedding
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 country.
