Human affect is an important consideration in several cognitive and AI-driven applications. Efficient and timely determination of affect is crucial in detecting various physiological and psychological illnesses affecting humans globally. Cognitive science advocates the use of different modalities such as voice, text, images, and gestures in initiating/promoting human cognition. Thus, multimodality is an inherent nature of cognition. In this work, we have performed experimentation on real-time voice samples collected from four participants and attempted to determine patterns of affect. With suitable use of spectrogram, chroma features, beats, etc., we present various visualizations that depict the inter-individual acoustic differences. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.