Efficiency-oriented approaches for self-supervised speech representation learning

• URL: http://arxiv.org/abs/2312.11142v1
• Date: Mon, 18 Dec 2023 12:32:42 GMT
• Title: Efficiency-oriented approaches for self-supervised speech representation learning
• Authors: Luis Lugo and Valentin Vielzeuf
• Abstract summary: Self-supervised learning enables the training of large neural models without the need for large, labeled datasets. It has been generating breakthroughs in several fields, including computer vision, natural language processing, biology, and speech. Despite current efforts, more work could be done to address high computational costs in self-supervised representation learning.
• Score: 1.860144985630098
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Self-supervised learning enables the training of large neural models without the need for large, labeled datasets. It has been generating breakthroughs in several fields, including computer vision, natural language processing, biology, and speech. In particular, the state-of-the-art in several speech processing applications, such as automatic speech recognition or speaker identification, are models where the latent representation is learned using self-supervised approaches. Several configurations exist in self-supervised learning for speech, including contrastive, predictive, and multilingual approaches. There is, however, a crucial limitation in most existing approaches: their high computational costs. These costs limit the deployment of models, the size of the training dataset, and the number of research groups that can afford research with large self-supervised models. Likewise, we should consider the environmental costs that high energy consumption implies. Efforts in this direction comprise optimization of existing models, neural architecture efficiency, improvements in finetuning for speech processing tasks, and data efficiency. But despite current efforts, more work could be done to address high computational costs in self-supervised representation learning.

Related papers

• Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application [17.367710635990083]
  We focus on natural language processing (NLP) and the role of large language models (LLMs) This paper discusses advanced data preprocessing techniques and the use of frameworks like Hugging Face for implementing transformer-based models. It highlights challenges such as handling multilingual data, reducing bias, and ensuring model robustness.
  arXiv  Detail & Related papers  (2024-10-30T09:35:35Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Unsupervised Data Validation Methods for Efficient Model Training [0.0]
  State-of-the-art models in natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT) and vision-language models (VLM) rely heavily on large datasets. This research explores key areas such as defining "quality data," developing methods for generating appropriate data and enhancing accessibility to model training.
  arXiv  Detail & Related papers  (2024-10-10T13:00:53Z)
• On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning [33.89483627891117]
  Recent advancements in language and vision assistants have showcased impressive capabilities but suffer from a lack of transparency. Open-source models handle general image tasks effectively, but face challenges with the high computational demands of complex visually-situated text understanding. This study aims to redefine the design of vision-language models by identifying key components and creating efficient models with constrained inference costs.
  arXiv  Detail & Related papers  (2024-06-17T17:57:30Z)
• Enhancing Large Vision Language Models with Self-Training on Image Comprehension [131.14381425260706]
  We introduce Self-Training on Image (STIC), which emphasizes a self-training approach specifically for image comprehension. First, the model self-constructs a preference for image descriptions using unlabeled images. To further self-improve reasoning on the extracted visual information, we let the model reuse a small portion of existing instruction-tuning data.
  arXiv  Detail & Related papers  (2024-05-30T05:53:49Z)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• eP-ALM: Efficient Perceptual Augmentation of Language Models [70.47962271121389]
  We propose to direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning.
  arXiv  Detail & Related papers  (2023-03-20T19:20:34Z)
• Self-Supervised Speech Representation Learning: A Review [105.1545308184483]
  Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods. This review presents approaches for self-supervised speech representation learning and their connection to other research areas.
  arXiv  Detail & Related papers  (2022-05-21T16:52:57Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)
• Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better [0.0]
  With the progressive improvements in deep learning models, their number of parameters, latency, resources required to train, etc. have increased significantly. We present and motivate the problem of efficiency in deep learning, followed by a thorough survey of the five core areas of model efficiency. We believe this is the first comprehensive survey in the efficient deep learning space that covers the landscape of model efficiency from modeling techniques to hardware support.
  arXiv  Detail & Related papers  (2021-06-16T17:31:38Z)

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.