Effect of different splitting criteria on the performance of speech emotion recognition

tldr: text-independent (TI) plays crucial role in SER. Different linguistic information in training and test phase resulted in different performance.

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B. T. Atmaja and A. Sasou, “Effect of different splitting criteria on the performance of speech emotion recognition,” In Proc. 2021 IEEE REGION 10 CONFERENCE (TENCON).

Name		Name	Last commit message	Last commit date
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data		data
exp1_30trials		exp1_30trials
exp2_cv		exp2_cv
exp3_same		exp3_same
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
bagus_tencon2021_v3.pdf		bagus_tencon2021_v3.pdf
bta_tencon_2021_v3.pdf		bta_tencon_2021_v3.pdf
classify_jtes_base.py		classify_jtes_base.py
classify_jtes_dense_opensmile.py		classify_jtes_dense_opensmile.py
classify_jtes_dense_opensmile_ti.py		classify_jtes_dense_opensmile_ti.py
classify_sd.py		classify_sd.py
classify_si.py		classify_si.py
classify_sti.py		classify_sti.py
classify_ti.py		classify_ti.py
classify_tib.py		classify_tib.py
csv2npy_hsf.py		csv2npy_hsf.py
csv2npy_hsf_sd.py		csv2npy_hsf_sd.py
csv2npy_hsf_sd2.py		csv2npy_hsf_sd2.py
extract_jtes_hsf.py		extract_jtes_hsf.py
extract_si.py		extract_si.py
extract_ti.py		extract_ti.py
note.md		note.md
td.py		td.py

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Effect of different splitting criteria on the performance of speech emotion recognition

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