How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario

• URL: http://arxiv.org/abs/2411.18217v2
• Date: Sun, 05 Jan 2025 13:07:32 GMT
• Title: How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario
• Authors: Shih-Heng Wang, Zih-Ching Chen, Jiatong Shi, Ming-To Chuang, Guan-Ting Lin, Kuan-Po Huang, David Harwath, Shang-Wen Li, Hung-yi Lee,
• Abstract summary: Speech Self-Supervised Learning (SSL) models achieve impressive performance on Automatic Speech Recognition (ASR) In low-resource language ASR, they encounter the domain mismatch problem between pre-trained and low-resource languages. We extend a conventional efficient fine-tuning scheme based on the adapter to handle these issues.
• Score: 72.02391485962127
• License:
• Abstract: The utilization of speech Self-Supervised Learning (SSL) models achieves impressive performance on Automatic Speech Recognition (ASR). However, in low-resource language ASR, they encounter the domain mismatch problem between pre-trained and low-resource languages. Typical solutions like fine-tuning the SSL model suffer from high computation costs while using frozen SSL models as feature extractors comes with poor performance. To handle these issues, we extend a conventional efficient fine-tuning scheme based on the adapter. We add an extra intermediate adaptation to warm up the adapter and downstream model initialization. Remarkably, we update only 1-5% of the total model parameters to achieve the adaptation. Experimental results on the ML-SUPERB dataset show that our solution outperforms conventional efficient fine-tuning. It achieves up to a 28% relative improvement in the Character/Phoneme error rate when adapting to unseen languages.

Related papers

• Unlocking the Potential of Model Merging for Low-Resource Languages [66.7716891808697]
  Adapting large language models to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT) We propose model merging as an alternative for low-resource languages, combining models with distinct capabilities into a single model without additional training. Experiments based on Llama-2-7B demonstrate that model merging effectively endows LLMs for low-resource languages with task-solving abilities, outperforming CT-then-SFT in scenarios with extremely scarce data.
  arXiv  Detail & Related papers  (2024-07-04T15:14:17Z)
• ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets [106.7760874400261]
  This paper presents ML-SUPERB2.0, which is a new benchmark for evaluating pre-trained SSL and supervised speech models. We find performance improvements over the setup of ML-SUPERB, but performance depends on the downstream model design. Also, we find large performance differences between languages and datasets, suggesting the need for more targeted approaches.
  arXiv  Detail & Related papers  (2024-06-12T21:01:26Z)
• Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification [19.893213508284813]
  Self-supervised adaptive pre-training is proposed to adapt the pre-trained model to the target domain and languages of the downstream task. We show that SAPT improves XLSR performance on the FLEURS benchmark with substantial gains up to 40.1% for under-represented languages.
  arXiv  Detail & Related papers  (2023-12-12T14:58:08Z)
• Efficiently Adapting Pretrained Language Models To New Languages [9.33333013114014]
  Recent large language models (LLM) exhibit sub-optimal performance on low-resource languages. We study how to efficiently adapt any existing pretrained LLM to a new language without running into these issues.
  arXiv  Detail & Related papers  (2023-11-09T20:59:08Z)
• CHAPTER: Exploiting Convolutional Neural Network Adapters for Self-supervised Speech Models [62.60723685118747]
  Self-supervised learning (SSL) is a powerful technique for learning representations from unlabeled data. We propose an efficient tuning method specifically designed for SSL speech model, by applying CNN adapters at the feature extractor. We empirically found that adding CNN to the feature extractor can help the adaptation on emotion and speaker tasks.
  arXiv  Detail & Related papers  (2022-12-01T08:50:12Z)
• Exploring Efficient-tuning Methods in Self-supervised Speech Models [53.633222197712875]
  Self-supervised learning can learn powerful representations for different speech tasks. In downstream tasks, the parameters of SSL models are frozen, and only the adapters are trained. We show that the performance parity can be achieved with over 90% parameter reduction.
  arXiv  Detail & Related papers  (2022-10-10T11:08:12Z)
• Factorized Neural Transducer for Efficient Language Model Adaptation [51.81097243306204]
  We propose a novel model, factorized neural Transducer, by factorizing the blank and vocabulary prediction. It is expected that this factorization can transfer the improvement of the standalone language model to the Transducer for speech recognition. We demonstrate that the proposed factorized neural Transducer yields 15% to 20% WER improvements when out-of-domain text data is used for language model adaptation.
  arXiv  Detail & Related papers  (2021-09-27T15:04:00Z)

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.