Transfer learning for end-to-end speech recognition and beyond

The continuous nature of speech presents unique challenges for information extraction and understanding, often requiring substantial data and computational resources. While recent advances in end-to-end neural architectures have improved speech processing capabilities, the scarcity of labeled data remains a significant constraint, particularly for tasks involving semantic understanding. The encoder-decoder architecture is a powerful paradigm for addressing these challenges, offering a natural separation between input encoding and output generation that proves particularly valuable for speech processing. This modular structure enables targeted transfer learning approaches and provides a framework for bridging different modalities, making it especially suitable for investigating how knowledge can be effectively transferred across speech and text domains.