Low-resource speech recognition and dialect identification of Irish in a multi-task framework
<|reference_start|>Low-resource speech recognition and dialect identification of Irish in a multi-task framework: This paper explores the use of Hybrid CTC/Attention encoder-decoder models trained with Intermediate CTC (InterCTC) for Irish (Gaelic) low-resource speech recognition (ASR) and dialect identification (DID). Results are compared to the current best performing models trained for ASR (TDNN-HMM) and DID (ECAPA-TDNN). An optimal InterCTC setting is initially established using a Conformer encoder. This setting is then used to train a model with an E-branchformer encoder and the performance of both architectures are compared. A multi-task fine-tuning approach is adopted for language model (LM) shallow fusion. The experiments yielded an improvement in DID accuracy of 10.8% relative to a baseline ECAPA-TDNN, and WER performance approaching the TDNN-HMM model. This multi-task approach emerges as a promising strategy for Irish low-resource ASR and DID.<|reference_end|>
@article{lonergan2024low-resource,
    title={Low-resource speech recognition and dialect identification of Irish in a
  multi-task framework},
    author={Liam Lonergan, Mengjie Qian, Neasa N'i Chiar'ain, Christer Gobl,
  Ailbhe N'i Chasaide},
    journal={arXiv preprint arXiv:2405.01293},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01293},
    primaryClass={cs.CL cs.AI cs.SD eess.AS}
}