Unsupervised Representations Improve Supervised Learning in Speech
  Emotion Recognition
Amirali Soltani Tehrani Niloufar Faridani Ramin Toosi
Speech Emotion Recognition (SER) plays a pivotal role in enhancing
human-computer interaction by enabling a deeper understanding of emotional
states across a wide range of applications, contributing to more empathetic and
effective communication. This study proposes an innovative approach that
integrates self-supervised feature extraction with supervised classification
for emotion recognition from small audio segments. In the preprocessing step,
to eliminate the need of crafting audio features, we employed a self-supervised
feature extractor, based on the Wav2Vec model, to capture acoustic features
from audio data. Then, the output featuremaps of the preprocessing step are fed
to a custom designed Convolutional Neural Network (CNN)-based model to perform
emotion classification. Utilizing the ShEMO dataset as our testing ground, the
proposed method surpasses two baseline methods, i.e. support vector machine
classifier and transfer learning of a pretrained CNN. comparing the propose
method to the state-of-the-art methods in SER task indicates the superiority of
the proposed method. Our findings underscore the pivotal role of deep
unsupervised feature learning in elevating the landscape of SER, offering
enhanced emotional comprehension in the realm of human-computer interactions.