speaker’s choices, and a binary reward can be the correctness of choice.

Speech synthesis and coding. Likewise, the states can be the input text, the actions can
be the generated audio, and the reward could be the similarity between the gold and generated
mel-spectrogram.

26

Mehrish et al.

Fig. 6. A standard experimental pipeline for GCNs, which embeds the graph node and embeds the graph
node edge features, performs several GNN layers to compute convolutional features, and finally predicts a
task-specific MLP layer.

Deep reinforcement learning has several advantages over traditional machine learning techniques.
It can learn from raw data without needing hand-engineered features, making it more flexible
and adaptable. It can also learn from feedback, making it more robust and able to handle noisy
environments.
AReviewofDeepLearningTechniquesforSpeechProcessing