| dc.description.abstract | Children who belong to autism spectrum
disorder have difficulty identifying emotions and
expressing their emotions. Because it is hard to identify the
emotions like anger, disgust, fear, happiness, neutral, sad,
and surprise in other people and themselves. This can be
even more severe when it could not be found at the
beginning and may lead to impairment of social
communication of the child. Through the proposed
systematic methodology child can identify their basic
emotions and try to express them. This evolved
methodology was developed using python language. For
emotion recognition used a deep machine learning model
like Recurrent Neural Network (RNN) using Keras with
a TensorFlow backend. RNN consists of four layers with
two long short-term memory (LSTM) layers. To
optimize the performance of the model used Adam
optimizer. For the training and testing of the model used
online available data. For the classification of the
emotion’s valuable features of the audio signal like Zero
Crossing Rate (ZCR), Chroma STFT, Mel- Frequency
Cepstral Coefficient (MFCC), Root Mean Square (RMS)
value, and Mel spectrogram are extracted using the
python libROSA library. Due to the lack of the data
amount and GPU requirements model’s performance
can be decreased. This model performed well with the
TESS data corpus with 91% test accuracy. | en_US |
