Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning

By Zheng Lian, Ya Li, Jianhua Tao, Jian Huang. 2018

18 citations recorded by the source corpus.

I have submitted a new version to arXiv:1910.13806. I forget to choose to replace the old version, but submitted a new one. It's my mistake.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Model Architectures, Speech & Audio, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ne Luo
2. Wubo Li
3. Wei Zou
4. Dongwei Jiang
5. Xiangang Li
6. Shiyu Zhou
7. Yuxuan Hu
8. Hanqing Lu

In Model Architectures

1. Shiyu Zhou
2. Hanqing Lu
3. Xiangang Li
4. Dongwei Jiang
5. Wei Zou
6. Jinqiao Wang

In Speech & Audio

1. Ne Luo
2. Wubo Li
3. Xiangang Li
4. Wei Zou
5. Dongwei Jiang
6. Shiyu Zhou
7. Hung-yi Lee

In Signal Processing

1. Ne Luo
2. Wubo Li
3. Wei Zou
4. Dongwei Jiang
5. Xiangang Li
6. Shiyu Zhou
7. Hanqing Lu
8. Hung-yi Lee

Citations in the corpus

• Improving Transformer-based Speech Recognition Using Unsupervised Pre-training (2019, background): “Recently, unsupervised pre-training has shown promising results in several areas, including Computer Vision (CV) [1], Natural Language Processing (NLP) [2, 3] and so on [4, 5, 6].”
• wav2vec: Unsupervised Pre-training for Speech Recognition (2019, neutral): “In speech processing, pre-training has focused on emotion recogniton (Lian et al., 2018), speaker identification (Ravanelli & Bengio, 2018), phoneme discrimination (Synnaeve & Dupoux, 2016a; van den Oord et al., 2018) as well as transferring ASR representations from one language to another (Kunze et…”
• A Further Study of Unsupervised Pretraining for Transformer Based Speech Recognition (2020, background): “There are multiple concurrent approaches that generalize this approach [4, 5] and applied it in learning speaker representation [6], extracting speech representation [7, 8], performing various speech-related tasks like speech recognition [7, 9, 10], speech emotion recognition [11] and so on.”
• Speech Recognition (2018, unknown): “In speech processing, pre-training has focused on emotion recogniton (Lian et al., 2018), speaker identification (Ravanelli & Bengio, 2018), phoneme discrimination (Synnaeve & Dupoux, 2016a; van den Oord et al., 2018) as well as transferring ASR representations from one language to another (Kunze…”
• OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation (2021, background): “In audio and speech processing, pre-training has focused on emotion recognition [24], speaker identification [32], phoneme discrimination [39, 27], transferring ASR representations from one language to another [17], unsupervised representations learning for speech [35], audio representation learning”