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README.md

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 # 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 all-age 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), [ESD](https://github.com/HLTSingapore/Emotional-Speech-Data), [IEMOCAP](https://sail.usc.edu/iemocap/iemocap_release.htm), and [TESS](https://www.kaggle.com/datasets/ejlok1/toronto-emotional-speech-set-tess).
+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), [ESD](https://github.com/HLTSingapore/Emotional-Speech-Data), [IEMOCAP](https://sail.usc.edu/iemocap/iemocap_release.htm), 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 script used for training and evaluation can be found here: