Audio Self-supervised Learning: A Survey

• URL: http://arxiv.org/abs/2203.01205v1
• Date: Wed, 2 Mar 2022 15:58:29 GMT
• Title: Audio Self-supervised Learning: A Survey
• Authors: Shuo Liu, Adria Mallol-Ragolta, Emilia Parada-Cabeleiro, Kun Qian, Xin Jing, Alexander Kathan, Bin Hu, Bjoern W. Schuller
• Abstract summary: Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations. Its success in the fields of computer vision and natural language processing have prompted its recent adoption into the field of audio and speech processing.
• Score: 60.41768569891083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations, which is an expensive and time consuming task. Its success in the fields of computer vision and natural language processing have prompted its recent adoption into the field of audio and speech processing. Comprehensive reviews summarising the knowledge in audio SSL are currently missing. To fill this gap, in the present work, we provide an overview of the SSL methods used for audio and speech processing applications. Herein, we also summarise the empirical works that exploit the audio modality in multi-modal SSL frameworks, and the existing suitable benchmarks to evaluate the power of SSL in the computer audition domain. Finally, we discuss some open problems and point out the future directions on the development of audio SSL.

Related papers

• Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding [14.468870364990291]
  We propose a novel Federated SSL (F-SSL) framework, dubbed FASSL, that enables learning intermediate feature representations from large-scale decentralized heterogeneous clients. Our study has found that audio F-SSL approaches perform on par with the centralized audio-SSL approaches on the audio-retrieval task.
  arXiv  Detail & Related papers  (2024-02-05T10:57:48Z)
• SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge? [45.901645659694935]
  Self-supervised learning (SSL) for speech representation has been successfully applied in various downstream tasks. In this paper, we aim to clarify if speech SSL techniques can well capture linguistic knowledge.
  arXiv  Detail & Related papers  (2023-06-14T09:04:29Z)
• Mediapipe and CNNs for Real-Time ASL Gesture Recognition [0.1529342790344802]
  This research paper describes a realtime system for identifying American Sign Language (ASL) movements. The suggested method makes use of the Mediapipe library for feature extraction and a Convolutional Neural Network (CNN) for ASL gesture classification.
  arXiv  Detail & Related papers  (2023-05-09T09:35:45Z)
• Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition? [86.53044183309824]
  We study which factor leads to the success of self-supervised learning on speaker-related tasks. Our empirical results on the Voxceleb-1 dataset suggest that the benefit of SSL to SV task is from a combination of mask speech prediction loss, data scale, and model size.
  arXiv  Detail & Related papers  (2022-04-27T08:35:57Z)
• Self-Supervised Learning for speech recognition with Intermediate layer supervision [52.93758711230248]
  We propose Intermediate Layer Supervision for Self-Supervised Learning (ILS-SSL) ILS-SSL forces the model to concentrate on content information as much as possible by adding an additional SSL loss on the intermediate layers. Experiments on LibriSpeech test-other set show that our method outperforms HuBERT significantly.
  arXiv  Detail & Related papers  (2021-12-16T10:45:05Z)
• WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing [102.45426364965887]
  We propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. We scale up the training dataset from 60k hours to 94k hours of public audio data, and optimize its training procedure for better representation extraction.
  arXiv  Detail & Related papers  (2021-10-26T17:55:19Z)
• UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training [72.004873454347]
  Two methods are introduced for enhancing the unsupervised speaker information extraction. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance. We scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement.
  arXiv  Detail & Related papers  (2021-10-12T05:43:30Z)
• LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech [63.84741259993937]
  Self-Supervised Learning (SSL) using huge unlabeled data has been successfully explored for image and natural language processing. Recent works also investigated SSL from speech. We propose LeBenchmark: a reproducible framework for assessing SSL from speech.
  arXiv  Detail & Related papers  (2021-04-23T08:27:09Z)

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.