Performance-Weighted Ensemble Learning for Speech Classification

Ensemble learning is a useful technique to combine several models to improve classification performance. Previous research on speech classification shows the benefit of ensemble learning over single models; however, there is no systematic evaluation on the accommodating single model performance in ensemble learning for speech classification. There is also no detailed report on the usefulness of ensemble learning over task-specific acoustic feature. We evaluate performance-weighted ensemble learning by taking into account the previous single model performance for speech classification tasks, including speech emotion recognition, laughter type classification, gender prediction and age prediction. We compare different weighting schemes based on unweighted and weighted accuracies, in which we also reported our results using these metrics. Results on six tasks and eleven datasets show diverse findings on the effectiveness of performance-weighted ensemble learning over other ensemble methods and single models.