The accuracy of automated speaker recognition is negatively impacted by change in emotions in a person's speech. In this thesis, we hypothesize that speaker identity is composed of various vocal style factors that may be learned from unlabeled speech data and re-combined using a neural network architecture to generate holistic speaker identity representations for affective scenarios. In this regard we propose the E-Vector neural network architecture, composed of a 1-D CNN for learning speaker identity features and a vocal style factorization technique for determining vocal styles. Experiments conducted on the MSP-Podcast dataset demonstrate that the proposed architecture improves state-of-the-art speaker recognition accuracy in the affective domain over baseline ECAPA-TDNN speaker recognition models. For instance, the true match rate at a false match rate of 1% improves from 27.6% to 46.2%. Additionally, we provide an analysis between speaker recognition match scores and emotions to identify challenging affective scenarios.
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