Abstract
Word recognition using deep learning is a simple approach to speech recognition in general. From this word-level recognition, the emotional expression recognition model. The emotion recognition model can be used to describe the important level of action on future planned hardware implementation. This research was conducted using MFCC as the feature extraction method from the audio data and using the CNN-LSTM approach for the emotional expression classifier. The model itself will be implemented into a humanoid robot to become a companion robot for the elderly. The model itself has 67% accuracy for emotion recognition and 97% accuracy for word recognition. However, the model only attained 20% accuracy in real-life testing using the humanoid robot as the model tends to overfitting as a result of the lack of data used in model training.
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