Self-Supervised Learning for Audio-Based Emotion Recognition

• URL: http://arxiv.org/abs/2307.12343v1
• Date: Sun, 23 Jul 2023 14:40:50 GMT
• Title: Self-Supervised Learning for Audio-Based Emotion Recognition
• Authors: Peranut Nimitsurachat and Peter Washington
• Abstract summary: Self-supervised learning is a family of methods which can learn despite a scarcity of supervised labels. We have applied self-supervised learning pre-training to the classification of emotions from the CMU- MOSEI's acoustic modality. We find that self-supervised learning consistently improves the performance of the model across all metrics.
• Score: 1.7598252755538808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion recognition models using audio input data can enable the development of interactive systems with applications in mental healthcare, marketing, gaming, and social media analysis. While the field of affective computing using audio data is rich, a major barrier to achieve consistently high-performance models is the paucity of available training labels. Self-supervised learning (SSL) is a family of methods which can learn despite a scarcity of supervised labels by predicting properties of the data itself. To understand the utility of self-supervised learning for audio-based emotion recognition, we have applied self-supervised learning pre-training to the classification of emotions from the CMU- MOSEI's acoustic modality. Unlike prior papers that have experimented with raw acoustic data, our technique has been applied to encoded acoustic data. Our model is first pretrained to uncover the randomly-masked timestamps of the acoustic data. The pre-trained model is then fine-tuned using a small sample of annotated data. The performance of the final model is then evaluated via several evaluation metrics against a baseline deep learning model with an identical backbone architecture. We find that self-supervised learning consistently improves the performance of the model across all metrics. This work shows the utility of self-supervised learning for affective computing, demonstrating that self-supervised learning is most useful when the number of training examples is small, and that the effect is most pronounced for emotions which are easier to classify such as happy, sad and anger. This work further demonstrates that self-supervised learning works when applied to embedded feature representations rather than the traditional approach of pre-training on the raw input space.

Related papers

• Self-supervised Learning for Acoustic Few-Shot Classification [10.180992026994739]
  We introduce and evaluate a new architecture that combines CNN-based preprocessing with feature extraction based on state space models (SSMs) We pre-train this architecture using contrastive learning on the actual task data and subsequent fine-tuning with an extremely small amount of labelled data. Our evaluation shows that it outperforms state-of-the-art architectures on the few-shot classification problem.
  arXiv  Detail & Related papers  (2024-09-15T07:45:11Z)
• EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training [79.96741042766524]
  We reformulate the training curriculum as a soft-selection function. We show that exposing the contents of natural images can be readily achieved by the intensity of data augmentation. The resulting method, EfficientTrain++, is simple, general, yet surprisingly effective.
  arXiv  Detail & Related papers  (2024-05-14T17:00:43Z)
• An Experimental Comparison Of Multi-view Self-supervised Methods For Music Tagging [6.363158395541767]
  Self-supervised learning has emerged as a powerful way to pre-train generalizable machine learning models on large amounts of unlabeled data. In this study, we investigate and compare the performance of new self-supervised methods for music tagging.
  arXiv  Detail & Related papers  (2024-04-14T07:56:08Z)
• Learning with Noisy Foundation Models [95.50968225050012]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets. We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• Phonetic and Prosody-aware Self-supervised Learning Approach for Non-native Fluency Scoring [13.817385516193445]
  Speech fluency/disfluency can be evaluated by analyzing a range of phonetic and prosodic features. Deep neural networks are commonly trained to map fluency-related features into the human scores. We introduce a self-supervised learning (SSL) approach that takes into account phonetic and prosody awareness for fluency scoring.
  arXiv  Detail & Related papers  (2023-05-19T05:39:41Z)
• Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training [66.80558875393565]
  We study the problem of training named entity recognition (NER) models using only distantly-labeled data. We propose a noise-robust learning scheme comprised of a new loss function and a noisy label removal step. Our method achieves superior performance, outperforming existing distantly-supervised NER models by significant margins.
  arXiv  Detail & Related papers  (2021-09-10T17:19:56Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning [0.0]
  We propose a novel transfer learning method for speech emotion recognition. With as low as 125 examples per emotion class, we were able to reach a higher accuracy than a strong baseline trained on 8 times more data.
  arXiv  Detail & Related papers  (2020-11-11T06:18:31Z)
• A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition [0.0]
  We show a transfer learning method in speech emotion recognition based on a Time-Delay Neural Network architecture. We achieve the highest significantly higher accuracy when compared to state-of-the-art, using five-fold cross validation.
  arXiv  Detail & Related papers  (2020-08-06T20:37:22Z)
• Automatic Recall Machines: Internal Replay, Continual Learning and the Brain [104.38824285741248]
  Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective. Instead the implicit memory of learned samples within the assessed model itself is exploited.
  arXiv  Detail & Related papers  (2020-06-22T15:07:06Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.