ABSTRACTSpeech has been widely known as the primary mode of communication among individuals and computers. The existence of technology brought about human computer interface to allow human computer interaction. While Speech Emotion Recognition Systems have been developed at a rapid pace over the last few years a lot of challenges has also been encountered during the course of this development such as inability to detect emotions that causes depression and mood swings which can be used by therapists to monitor the moods of their patients. To enhance the power of Speech Emotion Recognition models, it is required to design a model that recognizes the different emotions that leads to depression to enhance doctor-patient relationship. In this paper, we used the Knowledge Discovery Database (KDD) methodology and the features extracted were Zero Crossing Rate (ZCR), Mel-Frequency Cepstral Coefficients (MFCC) and Root Mean Square (RMS). The model was built using Tensorflow CONV1D with relu activation function and multiple sequential layers. An epoch size of 50 and a batch size of 64 were used. The result shows that using confusion matrix as the performance metrics yielded an accuracy of 96%.