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We propose a novel multi-task pre-training method for Speech Emotion Recognition (SER).
“The voice and the emotions,”
Smiley Blanton, · 1915
Earlier work this paper cites.
“An argument for basic emotions,”
Paul Ekman, · 1992
Earlier work this paper cites.
“Judgment of Emotion in Word-Free Voice Samples,”
William F. Soskin and Paul E. Kauffman, · 2006
Earlier work this paper cites.
“Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,”
Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber, · 2006
Earlier work this paper cites.
“Iemocap: Interactive emotional dyadic motion capture database,”
Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang, Sungbok Lee, and Shrikanth S Narayanan, · 2008
Earlier work this paper cites.
“Librispeech: An ASR corpus based on public domain audio books,”
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur, · 2015
Earlier work this paper cites.
“On the correlation and transferability of features between automatic speech recognition and speech emotion recognition.,”
Haytham M Fayek, Margaret Lech, and Lawrence Cavedon, · 2016
Earlier work this paper cites.
“Self-report captures 27 distinct categories of emotion bridged by continuous gradients,”
Alan S. Cowen and Dacher Keltner, · 2017
Earlier work this paper cites.
“Sentiment analysis on speaker specific speech data,”
S Maghilnan and M Rajesh Kumar, · 2017
Cited alongside, same era.
“Reusing neural speech representations for auditory emotion recognition,”
Egor Lakomkin, Cornelius Weber, Sven Magg, and Stefan Wermter, · 2018
Cited alongside, same era.
“Multimodal speech emotion recognition using audio and text,”
Seunghyun Yoon, Seokhyun Byun, and Kyomin Jung, · 2018
Cited alongside, same era.
“Representation learning with contrastive predictive coding,”
Aaron van den Oord, Yazhe Li, and Oriol Vinyals, · 2018
Cited alongside, same era.
“Exploring the intersection between speaker verification and emotion recognition,”
Michelle Bancroft, Reza Lotfian, John Hansen, and Carlos Busso, · 2019
Cited alongside, same era.
“X-vectors meet emotions: A study on dependencies between emotion and speaker recognition,”
Raghavendra Pappagari, Tianzi Wang, Jesus Villalba, Nanxin Chen, and Najim Dehak, · 2020
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“End-to-End Speech Emotion Recognition Combined with Acoustic-to-Word ASR Model,”
Han Feng, Sei Ueno, and Tatsuya Kawahara, · 2020
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“Tweeteval: Unified benchmark and comparative evaluation for tweet classification,”
Francesco Barbieri, José Camacho-Collados, Leonardo Neves, and Luis Espinosa Anke, · 2020
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“More than a feeling: Benchmarks for sentiment analysis accuracy,”
Mark Heitmann, Christian Siebert, Jochen Hartmann, and Christina Schamp, · 2020
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“Generative pre-training for speech with autoregressive predictive coding,”
Yu-An Chung and James Glass, · 2020
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“Building naturalistic emotionally balanced speech corpus by retrieving emotional speech from existing podcast recordings,”
R. Lotfian and C. Busso, · 2019
Cited alongside, same era.
“Specaugment: A simple data augmentation method for automatic speech recognition,”
Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le, · 2019
Cited alongside, same era.
“Detecting emotion primitives from speech and their use in discerning categorical emotions,”
Vasudha Kowtha, Vikramjit Mitra, Chris Bartels, Erik Marchi, Sue Booker, William Caruso, Sachin Kajarekar, and Devang Naik, · 2020
Cited alongside, same era.
“Contrastive unsupervised learning for speech emotion recognition,”
Mao Li, Bo Yang, Joshua Levy, Andreas Stolcke, Viktor Rozgic, Spyros Matsoukas, Constantinos Papayiannis, Daniel Bone, and Chao Wang, · 2021
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“Leveraging pre-trained language model for speech sentiment analysis,”
Suwon Shon, Pablo Brusco, Jing Pan, Kyu J. Han, and Shinji Watanabe, · 2021
Later among the works it cites.