Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey

TL;DR

This survey analyzes how advanced deep learning paradigms—DTL, FL, DRL, and Transformer-based architectures—are reshaping automatic speech recognition (ASR) in the face of data scarcity, privacy concerns, and domain mismatch. It provides a structured taxonomy, background on acoustic and language models, and a comparative review of AM/LM frameworks, including performance metrics such as $WER$ and $RTF$. The authors identify strengths, weaknesses, and open challenges across transformer-based ASR, cross-domain transfer, privacy-preserving distributed training, and reinforcement learning–driven optimization, offering concrete future directions like online DTL, multitask learning, and LLM-enhanced ASR. The practical impact lies in guiding researchers and practitioners toward robust, scalable, and privacy-aware ASR systems that generalize across languages, dialects, and real-world acoustic environments.

Abstract

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.

Figures (11)

• Figure 1: Summary of critical areas in speech processing where DTL, DRL, FL, and Transformers can be applied.
• Figure 2: Survey roadmap: A guide for navigating paper sections and subsections.
• Figure 3: Diagram illustrating the end-to-end framework for ASR.
• Figure 4: Overview of advanced DL-driven ASR algorithms and their commonly utilized models.
• Figure 5: Three forms of end-to-end Transformers models: (a) attention, (b) RNN-Transducer, and (c) basic CTC ahmed2023toward.