Transformers have evolved with great success in various artificial intelligence tasks. Thanks to our recent prevalence of self-attention mechanisms, which capture longterm dependency, phenomenal outcomes in speech processing and recognition tasks have been produced. The paper presents a comprehensive survey of transformer techniques oriented in speech modality. The main contents of this survey include (1) background of traditional ASR, end-to-end transformer ecosystem, and speech transformers (2) foundational models in a speech via lingualism paradigm, i.e., monolingual, bilingual, multilingual, and cross-lingual (3) dataset and languages, acoustic features, architecture, decoding, and evaluation metric from a specific topological lingualism perspective (4) popular speech transformer toolkit for building end-toend ASR systems. Finally, highlight the discussion of open challenges and potential research directions for the community to conduct further research in this domain.

Background: Majority of child deaths that occur worldwide are due to preventable causes. Maternal practices regarding children's health care have been recognized as an important factor behind mortality. Improving families' care-seeking behavior can significantly reduce child morbidity and mortality in developing countries.