Speaker Recognition (SR) is a critical component of digital speech processing. The robustness of Speaker Recognition systems is compromised by the variance in speakers’ emotional states. According to a study on SR utilizing emotive speech, it seems complicated to distinguish between emotions like “anger,” “sad,” “fear,” and “happy”. Developing a speaker recognition model that works effectively using emotional speech is challenging, specifically in the case of some intense emotions like anger. This work explores emotional speech transformation approaches to reduce the mismatch between training and testing emotional speech for the SR tasks. The recommended effort aims to develop speech transformation techniques to transform different emotional speech into anger. This study modifies the prosodic features “TPIB” (Tempo, Pitch, Intensity, and Brightness) to transform the speech from neutral, happy, fearful, and sad emotions to anger. Performance evaluations of the SR system employing transformed emotional speech are obtained through integrating Mel-Spectrogram feature extraction and deep learning techniques, including the CREMA-D and NITK-KLESC datasets. The experiment results demonstrate that the suggested emotional speech transformation technique increases SR accuracy in transforming neutral by approximately 15%, happy by 11%, sad by 32%, and fear by 30%.