Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization

• URL: http://arxiv.org/abs/2304.14535v2
• Date: Mon, 31 Jul 2023 11:58:18 GMT
• Title: Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization
• Authors: Hamza Kheddar, Yassine Himeur, Somaya Al-Maadeed, Abbes Amira, Faycal Bensaali
• Abstract summary: Speech recognition has become an important challenge when using deep learning (DL) It requires large-scale training datasets and high computational and storage resources. Deep transfer learning (DTL) has been introduced to overcome these issues.
• Score: 3.6393183544320236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input feature space and data distribution characteristics. This assumption, however, is not applicable in some real-world artificial intelligence (AI) applications. Moreover, there are situations where gathering real data is challenging, expensive, or rarely occurring, which can not meet the data requirements of DL models. deep transfer learning (DTL) has been introduced to overcome these issues, which helps develop high-performing models using real datasets that are small or slightly different but related to the training data. This paper presents a comprehensive survey of DTL-based ASR frameworks to shed light on the latest developments and helps academics and professionals understand current challenges. Specifically, after presenting the DTL background, a well-designed taxonomy is adopted to inform the state-of-the-art. A critical analysis is then conducted to identify the limitations and advantages of each framework. Moving on, a comparative study is introduced to highlight the current challenges before deriving opportunities for future research.

Related papers

• 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)
• Dynamic Data Pruning for Automatic Speech Recognition [58.95758272440217]
  We introduce Dynamic Data Pruning for ASR (DDP-ASR), which offers fine-grained pruning granularities specifically tailored for speech-related datasets. Our experiments show that DDP-ASR can save up to 1.6x training time with negligible performance loss.
  arXiv  Detail & Related papers  (2024-06-26T14:17:36Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation [8.727456619750983]
  The strategic integration of a visual prior into the training dataset emerges as a potential solution to enhance congruity with the testing data distribution. Our empirical evaluations underscore the efficacy of MISS, demonstrating commendable performance in scenarios characterized by limited data availability and memory constraints.
  arXiv  Detail & Related papers  (2024-03-18T08:52:23Z)
• Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey [2.716339075963185]
  Recent advancements in deep learning (DL) have posed a significant challenge for automatic speech recognition (ASR) ASR relies on extensive training datasets, including confidential ones, and demands substantial computational and storage resources. Advanced DL techniques like deep transfer learning (DTL), federated learning (FL), and reinforcement learning (RL) address these issues.
  arXiv  Detail & Related papers  (2024-03-02T16:25:42Z)
• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• Systems for Parallel and Distributed Large-Model Deep Learning Training [7.106986689736828]
  Some recent Transformer models span hundreds of billions of learnable parameters. These designs have introduced new scale-driven systems challenges for the DL space. This survey will explore the large-model training systems landscape, highlighting key challenges and the various techniques that have been used to address them.
  arXiv  Detail & Related papers  (2023-01-06T19:17:29Z)
• A Survey of Learning on Small Data: Generalization, Optimization, and Challenge [101.27154181792567]
  Learning on small data that approximates the generalization ability of big data is one of the ultimate purposes of AI. This survey follows the active sampling theory under a PAC framework to analyze the generalization error and label complexity of learning on small data. Multiple data applications that may benefit from efficient small data representation are surveyed.
  arXiv  Detail & Related papers  (2022-07-29T02:34:19Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• A Survey of Deep Active Learning [54.376820959917005]
  Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters. Deep active learning (DAL) has emerged.
  arXiv  Detail & Related papers  (2020-08-30T04:28:31Z)

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.