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README.md
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### Citation Info
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```
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@inproceedings{Amiriparian24-EEH,
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month = {September},
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publisher = {ISCA},
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```
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### References
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<small>
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-
B. Schuller, D. Arsic, G. Rigoll, M. Wimmer, and B. Radig. Audiovisual Behavior
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Modeling by Combined Feature Spaces. In 2007 IEEE International Conference on
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Acoustics, Speech and Signal Processing - ICASSP ’07, volume 2, pages II–733–II–
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736, Apr. 2007.
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<a id="2">[2]</a>
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M. Gerczuk, S. Amiriparian, S. Ottl, and B. W. Schuller. EmoNet: A Transfer
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Learning Framework for Multi-Corpus Speech Emotion Recognition. IEEE Trans-
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actions on Affective Computing, 14(2):1472–1487, Apr. 2023.
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<a id="3">[3]</a>
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T. L. Nwe, S. W. Foo, and L. C. De Silva. Speech emotion recognition using hidden
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Markov models. Speech Communication, 41(4):603–623, Nov. 2003.
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248 |
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249 |
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<a id="4">[4]</a>
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-
The selected speech emotion database of institute of automation chineseacademy of
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sciences (casia). http://www.chineseldc.org/resource_info.php?rid=76. accessed March 2024.
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<a id="5">[5]</a>
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P. Liu and M. D. Pell. Recognizing vocal emotions in Mandarin Chinese: A
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-
idated database of Chinese vocal emotional stimuli. Behavior Research Methods,
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44(4):1042–1051, Dec. 2012.
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H. Cao, D. G. Cooper, M. K. Keutmann, R. C. Gur, A. Nenkova, and R. Verma.
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CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset. IEEE transactions on affective computing, 5(4):377–390, 2014.
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<a id="7">[7]</a>
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I. S. Engberg, A. V. Hansen, O. K. Andersen, and P. Dalsgaard. Design
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269 |
-
ing and Verification of a Danish Emotional Speech Database: Design Recording
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270 |
-
and Verification of a Danish Emotional Speech Database. EUROSPEECH’97 : 5th
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-
European Conference on Speech Communication and Technology, Patras, Rhodes,
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Greece, 22-25 September 1997, pages Vol. 4, pp. 1695–1698, 1997.
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<a id="8">[8]</a>
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E. Parada-Cabaleiro, G. Costantini, A. Batliner, M. Schmitt, and B. W. Schuller.
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-
DEMoS: An Italian emotional speech corpus. Language Resources and Evaluation,
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54(2):341–383, June 2020.
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280 |
-
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281 |
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282 |
<a id="9">[9]</a>
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B. Schuller. Automatische Emotionserkennung Aus Sprachlicher Und Manueller
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284 |
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Interaktion. PhD thesis, Technische Universit¨at M¨unchen, 2006.
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<a id="10">[10]</a>
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F. Burkhardt, A. Paeschke, M. Rolfes, W. F. Sendlmeier, and B. Weiss. A database
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289 |
-
of German emotional speech. In Interspeech 2005, pages 1517–1520. ISCA, Sept.
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290 |
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2005.
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292 |
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<a id="11">[11]</a>
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294 |
-
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295 |
-
Categorical vs Dimensional Perception of Italian Emotional Speech. In Interspeech 2018,
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-
pages 3638–3642. ISCA, Sept. 2018.
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297 |
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<a id="12">[12]</a>
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A. Dhall, R. Goecke, J. Joshi, K. Sikka, and T. Gedeon. Emotion Recognition In
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The Wild Challenge 2014: Baseline, Data and Protocol. In Proceedings of the 16th
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International Conference on Multimodal Interaction, ICMI ’14, pages 461–466, New
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York, NY, USA, Nov. 2014. Association for Computing Machinery.
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<a id="13">[13]</a>
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G. Costantini, I. Iaderola, A. Paoloni, and M. Todisco. EMOVO Corpus: An Italian
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308 |
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Emotional Speech Database. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson,
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309 |
-
B. Maegaard, J. Mariani, A. Moreno, J. Odijk, and S. Piperidis, editors, Proceed-
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-
ings of the Ninth International Conference on Language Resources and Evaluation
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(LREC’14), pages 3501–3504, Reykjavik, Iceland, May 2014. European Language
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Resources Association (ELRA).
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-
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<a id="14">[14]</a>
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O. Martin, I. Kotsia, B. Macq, and I. Pitas. The eNTERFACE’ 05 Audio-Visual
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Emotion Database. In 22nd International Conference on Data Engineering Work-
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shops (ICDEW’06), pages 8–8, Apr. 2006.
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<a id="15">[15]</a>
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K. Zhou, B. Sisman, R. Liu, and H. Li. Seen and Unseen emotional style transfer
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-
for voice conversion with a new emotional speech dataset, Feb. 2021.
|
327 |
-
|
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|
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<a id="16">[16]</a>
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-
H. O’Reilly, D. Pigat, S. Fridenson, S. Berggren, S. Tal, O. Golan, S.
|
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-
Cohen, and D. Lundqvist. The EU-Emotion Stimulus Set: A validation study.
|
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-
Behavior Research Methods, 48(2):567–576, June 2016.
|
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-
|
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|
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<a id="17">[17]</a>
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A. Lassalle, D. Pigat, H. O’Reilly, S. Berggen, S. Fridenson-Hayo, S. Tal, S.
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-
A. R˚ade, O. Golan, S. B¨olte, S. Baron-Cohen, and D. Lundqvist. The EU-Emotion
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Voice Database. Behavior Research Methods, 51(2):493–506, Apr. 2019.
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<a id="18">[18]</a>
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A. Batliner, S. Steidl, and E. Noth. Releasing a thoroughly annotated and processed
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-
spontaneous emotional database: The FAU Aibo Emotion Corpus. 2008.
|
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|
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<a id="19">[19]</a>
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K. R. Scherer, T. B¨anziger, and E. Roesch. A Blueprint for Affective Computing:
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A Sourcebook and Manual. OUP Oxford, Sept. 2010.
|
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<a id="20">[20]</a>
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R. Banse and K. R. Scherer. Acoustic profiles in vocal emotion expression. Journal
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of Personality and Social Psychology, 70(3):614–636, 1996.
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<a id="21">[21]</a>
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C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang,
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-
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capture database. Language Resources and Evaluation, 42(4):335–359, Dec. 2008.
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<a id="22">[22]</a>
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M. M. Duville, L. M. Alonso-Valerdi, and D. I. Ibarra-Zarate. The Mexican
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-
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learning. Annual International Conference of the IEEE Engineering in Medicine
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and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual
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International Conference, 2021:1644–1647, Nov. 2021.
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<a id="23">[23]</a>
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S. Poria, D. Hazarika, N. Majumder, G. Naik, E. Cambria, and R. Mihalcea. MELD:
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2019.
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<a id="24">[24]</a>
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S. R. Livingstone and F. A. Russo. The Ryerson Audio-Visual Database of
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tional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal
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378 |
-
expressions in North American English. PLOS ONE, 13(5):e0196391, May 2018.
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<a id="25">[25]</a>
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S. Haq and P. J. B. Jackson. Speaker-dependent audio-visual emotion recognition.
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In Proc. AVSP 2009, pages 53–58, 2009.
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O. Mohamad Nezami, P. Jamshid Lou, and M. Karami. ShEMO: A large-scale
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validated database for Persian speech emotion detection. Language Resources and
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Evaluation, 53(1):1–16, Mar. 2019.
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International Conference on Language Resources and Evaluation (LREC’02), Las
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Palmas, Canary Islands - Spain, May 2002. European Language Resources Association (ELRA).
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B. Schuller, F. Eyben, S. Can, and H.
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J. H. L. Hansen and S. E. Bou-Ghazale. Getting started with SUSAS: A speech under
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simulated and actual stress database. In Proc. Eurospeech 1997, pages 1743–1746,
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S. Sultana, M. S. Rahman, M. R. Selim, and M. Z. Iqbal. SUST Bangla Emotional
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Speech Corpus (SUBESCO): An audio-only emotional speech corpus for Bangla.
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PLOS ONE, 16(4):e0250173, Apr. 2021.
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M. K. Pichora-Fuller and K. Dupuis. Toronto emotional speech set (TESS), Feb.
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2020.
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S. Latif, A. Qayyum, M. Usman, and J. Qadir. Cross Lingual Speech Emotion
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Recognition: Urdu vs. Western Languages. In 2018 International Conference on
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Frontiers of Information Technology (FIT), pages 88–93, Dec. 2018.
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<small>
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### Citation Info
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+
ExHuBERT has been accepted for presentation at INTERSPEECH 2024.
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```
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@article{amiriparian2024exhubert,
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title = {ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets},
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author = {Amiriparian, Shahin and Packa{\'n}, Filip and Gerczuk, Maurice and Schuller, Bj{\"o}rn W},
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journal= {arXiv preprint arXiv:2406.10275},
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year = {2024}
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}
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```
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<!--
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```
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@inproceedings{Amiriparian24-EEH,
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month = {September},
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publisher = {ISCA},
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}
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```
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-->
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### References
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<small>
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<a id="1">[1]</a>
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+
B. Schuller, D. Arsic, G. Rigoll, M. Wimmer, and B. Radig. Audiovisual Behavior Modeling by Combined Feature Spaces. Proc. ICASSP 2007, Apr. 2007.
|
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|
|
|
|
244 |
|
245 |
|
246 |
<a id="2">[2]</a>
|
247 |
+
M. Gerczuk, S. Amiriparian, S. Ottl, and B. W. Schuller. EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition. IEEE Transactions on Affective Computing, 14(2):1472–1487, Apr. 2023.
|
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|
|
248 |
|
249 |
|
250 |
<a id="3">[3]</a>
|
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+
T. L. Nwe, S. W. Foo, and L. C. De Silva. Speech emotion recognition using hidden Markov models. Speech Communication, 41(4):603–623, Nov. 2003.
|
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|
252 |
|
253 |
|
254 |
<a id="4">[4]</a>
|
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+
The selected speech emotion database of institute of automation chineseacademy of sciences (casia). http://www.chineseldc.org/resource_info.php?rid=76. accessed March 2024.
|
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|
256 |
|
257 |
|
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<a id="5">[5]</a>
|
259 |
+
P. Liu and M. D. Pell. Recognizing vocal emotions in Mandarin Chinese: A validated database of Chinese vocal emotional stimuli. Behavior Research Methods, 44(4):1042–1051, Dec. 2012.
|
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|
260 |
|
261 |
|
262 |
<a id="6">[6]</a>
|
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+
H. Cao, D. G. Cooper, M. K. Keutmann, R. C. Gur, A. Nenkova, and R. Verma. CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset. IEEE transactions on affective computing, 5(4):377–390, 2014.
|
|
|
264 |
|
265 |
|
266 |
|
267 |
<a id="7">[7]</a>
|
268 |
+
I. S. Engberg, A. V. Hansen, O. K. Andersen, and P. Dalsgaard. Design Recording and Verification of a Danish Emotional Speech Database: Design Recording and Verification of a Danish Emotional Speech Database. EUROSPEECH’97 : 5th European Conference on Speech Communication and Technology, Patras, Rhodes, Greece, 22-25 September 1997, pages Vol. 4, pp. 1695–1698, 1997.
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<a id="8">[8]</a>
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+
E. Parada-Cabaleiro, G. Costantini, A. Batliner, M. Schmitt, and B. W. Schuller. DEMoS: An Italian emotional speech corpus. Language Resources and Evaluation, 54(2):341–383, June 2020.
|
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|
274 |
|
275 |
<a id="9">[9]</a>
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+
B. Schuller. Automatische Emotionserkennung Aus Sprachlicher Und Manueller Interaktion. PhD thesis, Technische Universität München, 2006.
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|
277 |
|
278 |
|
279 |
<a id="10">[10]</a>
|
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+
F. Burkhardt, A. Paeschke, M. Rolfes, W. F. Sendlmeier, and B. Weiss. A database of German emotional speech. In Interspeech 2005, pages 1517–1520. ISCA, Sept. 2005.
|
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|
281 |
|
282 |
|
283 |
<a id="11">[11]</a>
|
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+
E. Parada-Cabaleiro, G. Costantini, A. Batliner, A. Baird, and B. Schuller. Categorical vs Dimensional Perception of Italian Emotional Speech. In Interspeech 2018, pages 3638–3642. ISCA, Sept. 2018.
|
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|
285 |
|
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|
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<a id="12">[12]</a>
|
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+
A. Dhall, R. Goecke, J. Joshi, K. Sikka, and T. Gedeon. Emotion Recognition In The Wild Challenge 2014: Baseline, Data and Protocol. In Proceedings of the 16th International Conference on Multimodal Interaction, ICMI ’14, pages 461–466, New York, NY, USA, Nov. 2014. Association for Computing Machinery.
|
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|
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<a id="13">[13]</a>
|
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+
G. Costantini, I. Iaderola, A. Paoloni, and M. Todisco. EMOVO Corpus: An Italian Emotional Speech Database. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, and S. Piperidis, editors, Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pages 3501–3504, Reykjavik, Iceland, May 2014. European Language Resources Association (ELRA).
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<a id="14">[14]</a>
|
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+
O. Martin, I. Kotsia, B. Macq, and I. Pitas. The eNTERFACE’ 05 Audio-Visual Emotion Database. In 22nd International Conference on Data Engineering Workshops (ICDEW’06), pages 8–8, Apr. 2006.
|
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|
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<a id="15">[15]</a>
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+
K. Zhou, B. Sisman, R. Liu, and H. Li. Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset, Feb. 2021.
|
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|
|
302 |
|
303 |
|
304 |
<a id="16">[16]</a>
|
305 |
+
H. O’Reilly, D. Pigat, S. Fridenson, S. Berggren, S. Tal, O. Golan, S. Bölte, S. Baron-Cohen, and D. Lundqvist. The EU-Emotion Stimulus Set: A validation study. Behavior Research Methods, 48(2):567–576, June 2016.
|
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|
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|
306 |
|
307 |
|
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<a id="17">[17]</a>
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A. Lassalle, D. Pigat, H. O’Reilly, S. Berggen, S. Fridenson-Hayo, S. Tal, S. Elfström, A. Rade, O. Golan, S. Bölte, S. Baron-Cohen, and D. Lundqvist. The EU-Emotion Voice Database. Behavior Research Methods, 51(2):493–506, Apr. 2019.
|
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|
310 |
|
311 |
|
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<a id="18">[18]</a>
|
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+
A. Batliner, S. Steidl, and E. Noth. Releasing a thoroughly annotated and processed spontaneous emotional database: The FAU Aibo Emotion Corpus. 2008.
|
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|
314 |
|
315 |
|
316 |
<a id="19">[19]</a>
|
317 |
+
K. R. Scherer, T. B¨anziger, and E. Roesch. A Blueprint for Affective Computing: A Sourcebook and Manual. OUP Oxford, Sept. 2010.
|
|
|
318 |
|
319 |
|
320 |
<a id="20">[20]</a>
|
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+
R. Banse and K. R. Scherer. Acoustic profiles in vocal emotion expression. Journal of Personality and Social Psychology, 70(3):614–636, 1996.
|
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|
322 |
|
323 |
|
324 |
<a id="21">[21]</a>
|
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+
C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan. IEMOCAP: Interactive emotional dyadic motion capture database. Language Resources and Evaluation, 42(4):335–359, Dec. 2008.
|
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+
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|
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<a id="22">[22]</a>
|
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+
M. M. Duville, L. M. Alonso-Valerdi, and D. I. Ibarra-Zarate. The Mexican Emotional Speech Database (MESD): Elaboration and assessment based on machine learning. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021:1644–1647, Nov. 2021.
|
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+
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<a id="23">[23]</a>
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+
S. Poria, D. Hazarika, N. Majumder, G. Naik, E. Cambria, and R. Mihalcea. MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations, June 2019.
|
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+
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335 |
|
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<a id="24">[24]</a>
|
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+
S. R. Livingstone and F. A. Russo. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLOS ONE, 13(5):e0196391, May 2018.
|
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|
338 |
|
339 |
|
340 |
<a id="25">[25]</a>
|
341 |
+
S. Haq and P. J. B. Jackson. Speaker-dependent audio-visual emotion recognition. In Proc. AVSP 2009, pages 53–58, 2009.
|
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342 |
|
343 |
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