Speech Recognition Transformers: Topological-lingualism Perspective
<|reference_start|>Speech Recognition Transformers: Topological-lingualism Perspective: 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.<|reference_end|>
@article{singh2024speech,
    title={Speech Recognition Transformers: Topological-lingualism Perspective},
    author={Shruti Singh, Muskaan Singh, Virender Kadyan},
    journal={arXiv preprint arXiv:2408.14991},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14991},
    primaryClass={cs.CL cs.SD eess.AS}
}