Low-resource speech recognition and dialect identification of Irish in a multi-task framework
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913339493318656 |
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| author | Lonergan, Liam Qian, Mengjie Chiaráin, Neasa Ní Gobl, Christer Chasaide, Ailbhe Ní |
| author_facet | Lonergan, Liam Qian, Mengjie Chiaráin, Neasa Ní Gobl, Christer Chasaide, Ailbhe Ní |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01293 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Low-resource speech recognition and dialect identification of Irish in a multi-task framework Lonergan, Liam Qian, Mengjie Chiaráin, Neasa Ní Gobl, Christer Chasaide, Ailbhe Ní Computation and Language Artificial Intelligence Sound Audio and Speech Processing 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. |
| title | Low-resource speech recognition and dialect identification of Irish in a multi-task framework |
| topic | Computation and Language Artificial Intelligence Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2405.01293 |