Speech Emotion Recognition (SER) is identification and classification of emotions from various speech signals with high precision and reliability and interpreting emotions more meaningfully in real-world scenarios. Traditionally,SER has focused on utterance level approaches,treating emotions as attributes of an entire spoken segment. Emotions in speech can also be dynamic and regarded as distinct scheduled events with clear time demarcations rather than generalized attributes. To deal with this Speech Emotion Diarization (SED) is introduced. Much like Speaker Diarization which pursues answer for the question,“Who speaks when?”, SED tackles “Which emotion appears when?”. SED focuses mainly on diarizing the input speech utterance into different emotions based on the occurrence of emotion. It also specially focuses on the individual frame analysis. It tells which emotion has occurred and when it has occurred. Emotion recognition and precise time segmentation is the key highlight of speech emotion diarization. SED can revolutionize and improve various aspects of our daily lives and also offers immense potential in fields such as enhanced mental health and well-being, call centers and customer support, education and e-learning, entertainment and media, safety and security, social robots, cross-cultural emotional understanding, and many more.