In this work, we explore the dependencies between speaker recognition and\nemotion recognition. We first show that knowledge learned for speaker\nrecognition can be reused for emotion recognition through transfer learning.\nThen, we show the effect of emotion on speaker recognition. For emotion\nrecognition, we show that using a simple linear model is enough to obtain good\nperformance on the features extracted from pre-trained models such as the\nx-vector model. Then, we improve emotion recognition performance by fine-tuning\nfor emotion classification. We evaluated our experiments on three different\ntypes of datasets: IEMOCAP, MSP-Podcast, and Crema-D. By fine-tuning, we\nobtained 30.40%, 7.99%, and 8.61% absolute improvement on IEMOCAP, MSP-Podcast,\nand Crema-D respectively over baseline model with no pre-training. Finally, we\npresent results on the effect of emotion on speaker verification. We observed\nthat speaker verification performance is prone to changes in test speaker\nemotions. We found that trials with angry utterances performed worst in all\nthree datasets. We hope our analysis will initiate a new line of research in\nthe speaker recognition community.\n