Self-supervised learning representations (SSLR) have become robust features for downstream tasks of many fields. Recently, several SSLRs have shown promising results on many automatic speech recognition (ASR) benchmark corpora. However, previous studies have only shown performance for solitary SSLRs as an input feature for ASR models. In this study, we propose to investigate the effectiveness of diverse SSLR combinations using various fusion methods within end-to-end (E2E) ASR models. In addition, we will show there are correlations between these extracted SSLRs. As such, we further propose a feature refinement loss for decorrelation to efficiently combine the set of input features. For evaluation, we show the proposed "fearless learning features" perform better than systems without the proposed feature refinement loss for both WSJ and Fearless Steps Challenge (FSC) corpora.