The development of Large Language Models (LLMs) has revolutionized Natural Language Processing (NLP), but they face significant challenges in analyzing human-to-human interactions, particularly in real-life contexts such as calls, meetings, and interviews. These limitations arise from discounting multimodal data and conversational cues from audio and voice modalities. LLMs struggle with temporal dependency, prosodic features, contextual understanding, and nuances of spoken language, including rhythm, timing, pitch analysis, volume, and emphasis. Inaccurate transcriptions due to lack of recognition of various accents or dialects can lead to errors in analysis and understanding. Moreover, the misinterpretation of emotions is another significant issue. The development of LLMs that can adequately analyze and comprehend spoken language in professional settings requires both technical and ethical considerations, including integrating emotion recognition algorithms and adopting multimodal analysis. A collaborative effort between speech scientists, AI engineers, and behavioral experts is necessary to address these shortcomings and create more holistic understanding models like Nebula, a proprietary large language model trained to perform generative tasks on human conversations.