Speech and Text-Based Emotion Recognizer
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Abstract (arXiv)
Affective computing is a field of study that focuses on developing systems and technologies that can understand, interpret, and respond to human emotions. Speech Emotion Recognition (SER), in particular, has got a lot of attention from researchers in the recent past. However, in many cases, the publicly available datasets, used for training and evaluation, are scarce and imbalanced across the emotion labels. In this work, we focused on building a balanced corpus from these publicly available datasets by combining these datasets as well as employing various speech data augmentation techniques. Furthermore, we experimented with different architectures for speech emotion recognition. Our best system, a multi-modal speech, and text-based model, provides a performance of UA(Unweighed Accuracy) + WA (Weighed Accuracy) of 157.57 compared to the baseline algorithm performance of 119.66
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References · 7
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
- 2018Attention Based Fully Convolutional Network for Speech Emotion Recognitioncited by 23
- 2016Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Datacited by 17
- 2021Speech Emotion Recognition with Multiscale Area Attention and Data Augmentationcited by 12
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