4.1 Supervised Learning
In supervised representation learning, the model is trained using annotated datasets to learn a
mapping between input data and output labels. The set of parameters that define the mapping
function is optimized during training to minimize the difference between the predicted and true
output labels in the training data. The goal of supervised representation learning is to enable the
model to learn a useful representation or features of the input data that can be used to accurately
predict the output label for new, unseen data. For instance, supervised representation learning
in speech processing using CNNs learn speech features from spectrograms. CNNs can identify
patterns in spectrograms relevant to speech recognition, such as those corresponding to different
phonemes or words. Unlike CNNs, which typically require spectrogram input, RNNs can directly
take in the raw speech signals as input and learn to extract features or representations that are
AReviewofDeepLearningTechniquesforSpeechProcessing