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--- |
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license: cc-by-4.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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dataset_info: |
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features: |
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- name: speaker_id |
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dtype: int64 |
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- name: path |
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dtype: audio |
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- name: duration |
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dtype: float64 |
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- name: accent |
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dtype: string |
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- name: emotion |
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dtype: string |
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- name: emotion_id |
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dtype: int64 |
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|
- name: gender |
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dtype: string |
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splits: |
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- name: train |
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|
num_bytes: 373345852.08 |
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num_examples: 5280 |
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download_size: 366955512 |
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dataset_size: 373345852.08 |
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--- |
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This dataset is part of a conference paper accepted to IEEE ICASSP 2024: [paper](https://ieeexplore.ieee.org/document/10448373). |
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Please cite as: |
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``` |
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@INPROCEEDINGS{10448373, |
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author={Thanh, Pham Viet and Huyen, Ngo Thi Thu and Quan, Pham Ngoc and Trang, Nguyen Thi Thu}, |
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booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
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title={A Robust Pitch-Fusion Model for Speech Emotion Recognition in Tonal Languages}, |
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year={2024}, |
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volume={}, |
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number={}, |
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pages={12386-12390}, |
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keywords={Emotion recognition;Video on demand;Pipelines;Speech recognition;Speech enhancement;Signal processing;Reliability engineering;Speech emotion recognition;vocal pitch;Vietnamese dataset;tonal languages}, |
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doi={10.1109/ICASSP48485.2024.10448373}} |
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``` |
