3.6 Reinforcement Learning
Reinforcement learning (RL) is a machine learning paradigm that trains an agent to perform
discrete actions in an environment and receive rewards or punishments based on its interactions.
The agent aims to learn a policy that maximizes its long-term reward. In recent years, RL has
become increasingly popular and has been applied to various domains, including robotics, game
playing, and natural language processing. RL has been utilized in speech recognition, speaker
diarization, and speech enhancement tasks in the speech field. One of the significant benefits of
using RL for speech tasks is its ability to learn directly from raw audio data, eliminating the need for
hand-engineered features. This can result in better performance compared to traditional methods
that rely on feature extraction. By capturing intricate patterns and relationships in the audio data,
RL-based speech systems have the potential to enhance accuracy and robustness.
AReviewofDeepLearningTechniquesforSpeechProcessing