Our project used machine learning to classify speech data by emotion. We use the CREMA-D dataset, which contains sentences spoken in different emotional tones by actors, and we explore how well we can classify emotion using features we extracted from the audio data. One of our classifiers was 5% more accurate than human raters of the same audios. The results of our project can help autistic children with emotional processing deficits perceive different emotional categories.