Update README.md

README.md

@@ -1,3 +1,41 @@
 ---
 license: cc-by-sa-4.0
+language:
+- en
+- zh
+metrics:
+- f1
+library_name: transformers
+pipeline_tag: audio-classification
+tags:
+- speech-emotion-recognition
 ---
+
+# Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English and Chinese data from elderly speakers.
+The model is trained on the training sets of [CREMA-D](https://github.com/CheyneyComputerScience/CREMA-D), [CSED](https://github.com/AkishinoShiame/Chinese-Speech-Emotion-Datasets), [ElderReact](https://github.com/Mayer123/ElderReact), and [TESS](https://www.kaggle.com/datasets/ejlok1/toronto-emotional-speech-set-tess).
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+The scripts used for training and evaluation can be found here:
+[https://github.com/HLTCHKUST/elderly_ser/tree/main](https://github.com/HLTCHKUST/elderly_ser/tree/main)
+
+## Evaluation Results
+
+For the details (e.g., the statistics of `train`, `valid`, and `test` data), please refer to our paper on [arXiv](https://arxiv.org/abs/2306.14517).
+It also provides the model's speech emotion recognition performances on: English-All, Chinese-All, English-Elderly, Chinese-Elderly, English-Adults, Chinese-Adults.
+
+## Citation
+
+Our paper will be published at INTERSPEECH 2023. In the meantime, you can find our paper on [arXiv](https://arxiv.org/abs/2306.14517).
+If you find our work useful, please consider citing our paper as follows:
+```
+@misc{cahyawijaya2023crosslingual,
+      title={Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition}, 
+      author={Samuel Cahyawijaya and Holy Lovenia and Willy Chung and Rita Frieske and Zihan Liu and Pascale Fung},
+      year={2023},
+      eprint={2306.14517},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```