  Speech-to-text translation pertains to the task of converting speech signals
in a language to text in another language. It finds its application in various
domains, such as hands-free communication, dictation, video lecture
transcription, and translation, to name a few. Automatic Speech Recognition
(ASR), as well as Machine Translation(MT) models, play crucial roles in
traditional ST translation, enabling the conversion of spoken language in its
original form to written text and facilitating seamless cross-lingual
communication. ASR recognizes spoken words, while MT translates the transcribed
text into the target language. Such disintegrated models suffer from cascaded
error propagation and high resource and training costs. As a result,
researchers have been exploring end-to-end (E2E) models for ST translation.
However, to our knowledge, there is no comprehensive review of existing works
on E2E ST. The present survey, therefore, discusses the work in this direction.
Our attempt has been to provide a comprehensive review of models employed,
metrics, and datasets used for ST tasks, providing challenges and future
research direction with new insights. We believe this review will be helpful to
researchers working on various applications of ST models.
