An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution

• URL: http://arxiv.org/abs/2404.07575v2
• Date: Fri, 12 Apr 2024 01:22:47 GMT
• Title: An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution
• Authors: Tien-Hong Lo, Fu-An Chao, Tzu-I Wu, Yao-Ting Sung, Berlin Chen,
• Abstract summary: Self-supervised learning (SSL) has shown stellar performance compared to traditional methods. However, SSL-based ASA systems are faced with at least three data-related challenges. These challenges include limited annotated data, uneven distribution of learner proficiency levels and non-uniform score intervals between different CEFR proficiency levels.
• Score: 5.1660803395535835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner's speech. Recently, self-supervised learning (SSL) has shown stellar performance compared to traditional methods. However, SSL-based ASA systems are faced with at least three data-related challenges: limited annotated data, uneven distribution of learner proficiency levels and non-uniform score intervals between different CEFR proficiency levels. To address these challenges, we explore the use of two novel modeling strategies: metric-based classification and loss reweighting, leveraging distinct SSL-based embedding features. Extensive experimental results on the ICNALE benchmark dataset suggest that our approach can outperform existing strong baselines by a sizable margin, achieving a significant improvement of more than 10% in CEFR prediction accuracy.

Related papers

• Co-training for Low Resource Scientific Natural Language Inference [65.37685198688538]
  We propose a novel co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels. By assigning importance weights instead of filtering out examples based on an arbitrary threshold on the predicted confidence, we maximize the usage of automatically labeled data. The proposed method obtains an improvement of 1.5% in Macro F1 over the distant supervision baseline, and substantial improvements over several other strong SSL baselines.
  arXiv  Detail & Related papers  (2024-06-20T18:35:47Z)
• Using Self-supervised Learning Can Improve Model Fairness [10.028637666224093]
  Self-supervised learning (SSL) has become the de facto training paradigm of large models. This study explores the impact of pre-training and fine-tuning strategies on fairness. We introduce a fairness assessment framework for SSL, comprising five stages: defining dataset requirements, pre-training, fine-tuning with gradual unfreezing, assessing representation similarity conditioned on demographics, and establishing domain-specific evaluation processes.
  arXiv  Detail & Related papers  (2024-06-04T14:38:30Z)
• A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification [61.473485511491795]
  Semi-supervised learning (SSL) is a practical challenge in computer vision. Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of The Art (SOTA) performances in SSL. We propose a lightweight channel-based ensemble method to consolidate multiple inferior PLs into the theoretically guaranteed unbiased and low-variance one.
  arXiv  Detail & Related papers  (2024-03-27T09:49:37Z)
• HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models [81.56455625624041]
  We introduce the first open-source benchmark to utilize external large language models (LLMs) for ASR error correction. The proposed benchmark contains a novel dataset, HyPoradise (HP), encompassing more than 334,000 pairs of N-best hypotheses. LLMs with reasonable prompt and its generative capability can even correct those tokens that are missing in N-best list.
  arXiv  Detail & Related papers  (2023-09-27T14:44:10Z)
• Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition [1.0705399532413615]
  Self-supervised learning (SSL) to learn high-level speech representations has been a popular approach to building Automatic Speech Recognition systems. We study the effect of domain, language, dataset size, and other aspects of our upstream pre-training SSL data on the final performance low-resource downstream ASR task.
  arXiv  Detail & Related papers  (2022-03-31T11:48:24Z)
• A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings [53.120885867427305]
  Three approaches are evaluated for speaker-attributed automatic speech recognition (SA-ASR) in a meeting scenario. The WD-SOT approach achieves 10.7% relative reduction on averaged speaker-dependent character error rate (SD-CER) The TS-ASR approach also outperforms the FD-SOT approach and brings 16.5% relative average SD-CER reduction.
  arXiv  Detail & Related papers  (2022-03-31T06:39:14Z)
• Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder [23.860842627883187]
  We teach the model to capture broader variations of the feature distributions with a novel noise-enhanced supervised autoencoder (NSAE) NSAE trains the model by jointly reconstructing inputs and predicting the labels of inputs as well as their reconstructed pairs. We also take advantage of NSAE structure and propose a two-step fine-tuning procedure that achieves better adaption and improves classification performance in the target domain.
  arXiv  Detail & Related papers  (2021-08-11T04:45:56Z)
• A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification [38.68079253627819]
  Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.
  arXiv  Detail & Related papers  (2021-04-01T17:59:41Z)
• Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training [55.824641135682725]
  Domain adaptation experiments using WSJ as a source domain and TED-LIUM 3 as well as SWITCHBOARD show that up to 80% of the performance of a system trained on ground-truth data can be recovered.
  arXiv  Detail & Related papers  (2020-11-26T18:51:26Z)
• Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition [6.2997667081978825]
  This paper presents a novel generic and robust approach for semi-supervised domain adaptation in Human Activity Recognition. It capitalizes on the advantages of the adversarial framework to tackle the shortcomings, by leveraging knowledge from annotated samples exclusively from the source subject and unlabeled ones of the target subject. The results demonstrate the effectiveness of our proposed algorithms over state-of-the-art methods, which led in up to 13%, 4%, and 13% improvement of our high-level activities recognition metrics for Opportunity, LISSI, and PAMAP2 datasets.
  arXiv  Detail & Related papers  (2020-11-11T12:16:23Z)
• Joint Contextual Modeling for ASR Correction and Language Understanding [60.230013453699975]
  We propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with language understanding (LU) We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
  arXiv  Detail & Related papers  (2020-01-28T22:09:25Z)

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.