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# System prepended metadata

title: 李弘毅

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# 李弘毅

## Fundation Models for Speech Model
- pretrain -> downstream
- tasks:
    - speech recognition
    - speaker recognision
- [SSS representation approaches](https://aclanthology.org/2022.naacl-tutorials.2/)

## SQA
- No transcription needed
    - I: q, doc
    - O: a
- Time Span
- NMSQA
- [SLUE-SQA-5](https://arxiv.org/pdf/2212.10525.pdf)
- Direct train:
    - content OK, bad at semantic
- Inprovement
    - add attention layers? X
    - cross dicipline capability
        - e.g. using english word to represent ATCG in DNA
    - HuBERT(Speech fundation model)+Longformer(Text fundation model)
    - 0.06 -> 0.5 F1 score
    - https://github.com/ga642381/SpeechPrompt

## Q
- how to map id and vowl embedding?
    - random
    - not special token
    - more frequent token -> not improved so much