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ViSEC

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train · 5.28k rows

speaker_id int64 0 146	duration float64 1 24.7	accent stringclasses 3 values	emotion stringclasses 4 values	emotion_id int64 0 3	gender stringclasses 2 values
0	2.508063	south	happy	0	female
1	1.6	south	neutral	1	male
2	1.472	south	angry	3	female
3	3.648	north	happy	0	female
4	3.584	south	neutral	1	male
0	1.663	south	angry	3	female
5	1.088	south	neutral	1	female
6	1.6	mid	neutral	1	female
7	5.056	south	neutral	1	male
0	1.922	south	angry	3	female
8	1.6	south	neutral	1	female
0	1.483	south	angry	3	female
9	2.368	south	sad	2	female
0	2.19	south	neutral	1	female
0	1.71	south	sad	2	female
10	2.368	north	neutral	1	male
11	2.752	south	neutral	1	male
12	6.656	north	angry	3	male
0	1.04	south	angry	3	female
0	2.12	south	neutral	1	female
6	1.472	mid	neutral	1	female
0	1.98	south	angry	3	female
0	2.1	south	angry	3	female
0	1.34	south	happy	0	female
0	1.26	south	happy	0	female
13	1.28	south	angry	3	female
12	1.792	north	sad	2	male
14	2.624	north	happy	0	male
15	2.816	south	happy	0	male
15	1.28	south	neutral	1	male
15	1.216	south	sad	2	male
0	1.113	south	sad	2	female
0	1.72	south	sad	2	female
0	1.8	south	sad	2	female
16	1.472	north	sad	2	female
0	1.22	south	happy	0	female
12	1.536	north	angry	3	male
17	1.984	mid	sad	2	female
0	2.06	south	neutral	1	female
0	1.127	south	angry	3	female
16	1.6	north	neutral	1	female
18	1.792	north	neutral	1	male
0	1.561	south	happy	0	female
3	3.776	north	angry	3	female
0	1.388	south	angry	3	female
0	1.22	south	happy	0	female
19	3.2	north	neutral	1	female
0	1.643	south	sad	2	female
20	2.176	south	happy	0	male
21	4.032	south	happy	0	male
22	4.416	north	angry	3	female
0	2.914	south	happy	0	female
0	2.18	south	neutral	1	female
23	3.264	south	sad	2	female
6	3.008	mid	happy	0	female
15	1.472	south	neutral	1	male
0	1.52	south	sad	2	female
0	1.35	south	sad	2	female
0	1.706063	south	angry	3	female
3	3.584	north	neutral	1	female
12	2.88	north	happy	0	male
0	1.293063	south	happy	0	female
15	2.368	south	happy	0	male
24	1.792	south	angry	3	male
15	1.472	south	happy	0	male
0	1.14	south	sad	2	female
25	1.344	north	happy	0	male
26	3.712	north	happy	0	male
27	1.6	north	angry	3	male
6	1.728	mid	happy	0	female
14	2.304	north	angry	3	male
0	1.751	south	happy	0	female
2	2.496	south	sad	2	female
12	2.56	north	happy	0	male
28	1.92	south	neutral	1	male
12	2.368	north	angry	3	male
0	1.406063	south	angry	3	female
0	2.12	south	neutral	1	female
1	1.088	south	neutral	1	male
6	1.536	mid	sad	2	female
6	3.136	mid	neutral	1	female
0	1.24	south	sad	2	female
29	2.368	north	sad	2	female
30	2.56	north	neutral	1	female
22	2.368	north	angry	3	female
31	3.072	north	happy	0	female
0	1.653	south	angry	3	female
0	2.47	south	angry	3	female
1	1.792	south	neutral	1	male
32	3.2	south	sad	2	female
0	2.129063	south	neutral	1	female
33	8	mid	happy	0	female
0	1.622	south	angry	3	female
0	2.477063	south	angry	3	female
15	1.152	south	neutral	1	male
15	1.28	south	happy	0	male
0	1.445	south	angry	3	female
29	4.992	north	neutral	1	female
0	1.325063	south	angry	3	female
0	1.353	south	angry	3	female

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This dataset is part of a conference paper accepted to IEEE ICASSP 2024: paper.
Please cite as:

@INPROCEEDINGS{10448373,
  author={Thanh, Pham Viet and Huyen, Ngo Thi Thu and Quan, Pham Ngoc and Trang, Nguyen Thi Thu},
  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={A Robust Pitch-Fusion Model for Speech Emotion Recognition in Tonal Languages}, 
  year={2024},
  volume={},
  number={},
  pages={12386-12390},
  keywords={Emotion recognition;Video on demand;Pipelines;Speech recognition;Speech enhancement;Signal processing;Reliability engineering;Speech emotion recognition;vocal pitch;Vietnamese dataset;tonal languages},
  doi={10.1109/ICASSP48485.2024.10448373}}

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