  Recent advancements in deep learning (DL) have posed a significant challenge
for automatic speech recognition (ASR). ASR relies on extensive training
datasets, including confidential ones, and demands substantial computational
and storage resources. Enabling adaptive systems improves ASR performance in
dynamic environments. DL techniques assume training and testing data originate
from the same domain, which is not always true. Advanced DL techniques like
deep transfer learning (DTL), federated learning (FL), and reinforcement
learning (RL) address these issues. DTL allows high-performance models using
small yet related datasets, FL enables training on confidential data without
dataset possession, and RL optimizes decision-making in dynamic environments,
reducing computation costs. This survey offers a comprehensive review of DTL,
FL, and RL-based ASR frameworks, aiming to provide insights into the latest
developments and aid researchers and professionals in understanding the current
challenges. Additionally, transformers, which are advanced DL techniques
heavily used in proposed ASR frameworks, are considered in this survey for
their ability to capture extensive dependencies in the input ASR sequence. The
paper starts by presenting the background of DTL, FL, RL, and Transformers and
then adopts a well-designed taxonomy to outline the state-of-the-art
approaches. Subsequently, a critical analysis is conducted to identify the
strengths and weaknesses of each framework. Additionally, a comparative study
is presented to highlight the existing challenges, paving the way for future
research opportunities.
