# Speech Recognition Transformers: Topological-lingualism Perspective

arXiv: https://arxiv.org/abs/2408.14991 · HTML: https://mlsanity.com/paper/2408.14991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2024.

## Abstract (arXiv)

Transformers have evolved with great success in various artificial intelligence tasks. Thanks to our recent prevalence of self-attention mechanisms, which capture long-term 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-to-end ASR systems. Finally, highlight the discussion of open challenges and potential research directions for the community to conduct further research in this domain.