Update README.md

README.md

@@ -1,3 +1,31 @@
 ---
+language:
+  - multilingual
+  - ain
 license: apache-2.0
 ---
+
+## Wav2Vec2-Large-XLSR-53 fine-tuned for automatic transcription of Sakhalin Ainu
+
+This is a [wav2vec-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model after continued pretraining on speech data in Hokkaido Ainu and Sakhalin Ainu (see [wav2vec2-large-xlsr-53-pretrain-ain](https://huggingface.co/karolnowakowski/wav2vec2-large-xlsr-53-pretrain-ain)) and fine-tuning for automatic speech recognition on 10h of labeled Sakhalin Ainu data.
+For details, please refer to the [paper](https://arxiv.org/abs/2301.07295).
+
+
+## Citation
+When using the model please cite the following paper:
+```bibtex
+@article{NOWAKOWSKI2023103148,
+title = {Adapting multilingual speech representation model for a new, underresourced language through multilingual fine-tuning and continued pretraining},
+journal = {Information Processing & Management},
+volume = {60},
+number = {2},
+pages = {103148},
+year = {2023},
+issn = {0306-4573},
+doi = {https://doi.org/10.1016/j.ipm.2022.103148},
+url = {https://www.sciencedirect.com/science/article/pii/S0306457322002497},
+author = {Karol Nowakowski and Michal Ptaszynski and Kyoko Murasaki and Jagna Nieuważny},
+keywords = {Automatic speech transcription, ASR, Wav2vec 2.0, Pretrained transformer models, Speech representation models, Cross-lingual transfer, Language documentation, Endangered languages, Underresourced languages, Sakhalin Ainu},
+abstract = {In recent years, neural models learned through self-supervised pretraining on large scale multilingual text or speech data have exhibited promising results for underresourced languages, especially when a relatively large amount of data from related language(s) is available. While the technology has a potential for facilitating tasks carried out in language documentation projects, such as speech transcription, pretraining a multilingual model from scratch for every new language would be highly impractical. We investigate the possibility for adapting an existing multilingual wav2vec 2.0 model for a new language, focusing on actual fieldwork data from a critically endangered tongue: Ainu. Specifically, we (i) examine the feasibility of leveraging data from similar languages also in fine-tuning; (ii) verify whether the model’s performance can be improved by further pretraining on target language data. Our results show that continued pretraining is the most effective method to adapt a wav2vec 2.0 model for a new language and leads to considerable reduction in error rates. Furthermore, we find that if a model pretrained on a related speech variety or an unrelated language with similar phonological characteristics is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance when there is very little labeled data in the target language.}
+}
+```