Editing Development of End-to-End Models (section)

== Future research ==
Evolving end-to-end speech recognition modeling heralds a promising path of exploration and innovation. This section will cover several emerging directions, including issues ranging from robustness in unfavorable acoustic environments to the ethical and privacy considerations inherent in a wide range of applications. In addition, they include the quest for enhanced adaptability, the pursuit of multimodal integration, and the relentless pursuit of more accurate and contextualized transcription. Adoption of these future studies will form the basis for the next phase of end-to-end speech recognition paradigm innovation.

==== Robustness in Noisy Acoustic Environments: ====
An overarching issue in ASR research is how to strengthen models from the deleterious effects of acoustic perturbations, especially in real-world scenarios characterized by external noise sources. Research efforts could focus on techniques to improve the adaptability of end-to-end ASR systems to various ambient acoustic environments.<ref>K. N. Watcharasupat, T. N. T. Nguyen, W. -S. Gan, S. Zhao and B. Ma, "End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, 2022, pp. 656-660. https://ieeexplore.ieee.org/abstract/document/9747034</ref>

==== Low-Resource and Under-Resourced Linguistic Contexts: ====
Due to the lack of annotated data for training automatic recognition models for marginalized or low-resource dialects of languages, future research efforts may turn to methods for improving automatic recognition performance in the domain of resource-scarce languages. In addition to unsupervised or semi-supervised learning frameworks, this may include the judicious use of transfer learning paradigms for resource-rich languages.<ref>D. Wang, J. Yu, X. Wu, L. Sun, X. Liu and H. Meng, "Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initialization," 2021 12th International Symposium on Chinese Spoken Language Processing (ISCSLP), Hong Kong, 2021, pp. 1-5.https://ieeexplore.ieee.org/abstract/document/9362068</ref>

==== Multimodal Convergence: ====
The confluence of diverse modalities, such as audio, visual, and textual cues, embodies an incipient frontier in ASR research. <ref>S. Palaskar, R. Sanabria and F. Metze, "End-to-end Multimodal Speech Recognition," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018, pp. 5774-5778.https://ieeexplore.ieee.org/abstract/document/8462439</ref>Forward-looking investigations may be focused on the conceptualization and realization of end-to-end models adept at assimilating and fusing information gleaned from these heterogeneous sources to engender a more holistic comprehension of the targeted input.

==== Ethical and Privacy Implications: ====
As ASR technology becomes more widespread, ethical considerations are increasing. Research efforts may include the development of privacy-protective ASR methods, as well as ethical issues related to data collection, minimizing bias, and ensuring fairness in the operation of ASR systems.<ref>Feng, S., Kudina, O., Halpern, B. M., & Scharenborg, O. (2021). ''Quantifying Bias in Automatic Speech Recognition'' (arXiv:2103.15122). http://arxiv.org/abs/2103.15122</ref>

==== Interpretability and Explicability of Models: ====
Interrogating the rationales underpinning model predictions constitutes an essential endeavor, particularly in high-stakes contexts such as healthcare or legal transcription. Future research might orient itself toward the development of techniques that render end-to-end ASR models more amenable to interpretability.

==== Zero-Shot and Few-Shot Learning Paradigms: ====
Investigation into the development of models capable of generalizing to novel, unobserved tasks or languages with scant training data is a paramount research trajectory. Zero-shot and few-shot learning paradigms are likely to be pivotal in achieving this laudable objective.

==== Deployment and Practical Applicability: ====
Research efforts will be dedicated to the operationalization of end-to-end ASR systems in real-world contexts, necessitating considerations regarding computational efficiency, latency constraints, and adaptability to specific application domains.

These prospective avenues of inquiry underscore the unfolding trajectory of end-to-end ASR research, emblematic of a concerted effort to address salient real-world challenges and to harness the full potential of this technology.