With the rise of generative models, multi-modal video generation has gained significant attention, particularly in the realm of audio-driven emotional talking face synthesis. This paper addresses two key challenges in this domain: input bias and intensity saturation. A neutralization scheme is proposed to counter input bias, yielding impressive results in generating neutral talking faces from emotionally expressive ones. Furthermore, 2D continuous emotion label-based regression learning effectively generates varying emotional intensities through frame-wise comparisons. Results from a user study quantify subjective interpretations of strong emotions and naturalness, revealing up to 78.09% higher emotion accuracy and up to 3.41 higher naturalness compared to the lowest-ranked method.