Exploring the Potential of Large Multimodal Models as Effective Alternatives for Pronunciation Assessment

• URL: http://arxiv.org/abs/2503.11229v1
• Date: Fri, 14 Mar 2025 09:26:07 GMT
• Title: Exploring the Potential of Large Multimodal Models as Effective Alternatives for Pronunciation Assessment
• Authors: Ke Wang, Lei He, Kun Liu, Yan Deng, Wenning Wei, Sheng Zhao,
• Abstract summary: Large Multimodal Models (LMMs) have demonstrated exceptional performance across a wide range of domains.<n>This paper explores their potential in pronunciation assessment tasks, with a particular focus on evaluating the capabilities of the Generative Pre-trained Transformer (GPT) model.
• Score: 25.13605642785304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Multimodal Models (LMMs) have demonstrated exceptional performance across a wide range of domains. This paper explores their potential in pronunciation assessment tasks, with a particular focus on evaluating the capabilities of the Generative Pre-trained Transformer (GPT) model, specifically GPT-4o. Our study investigates its ability to process speech and audio for pronunciation assessment across multiple levels of granularity and dimensions, with an emphasis on feedback generation and scoring. For our experiments, we use the publicly available Speechocean762 dataset. The evaluation focuses on two key aspects: multi-level scoring and the practicality of the generated feedback. Scoring results are compared against the manual scores provided in the Speechocean762 dataset, while feedback quality is assessed using Large Language Models (LLMs). The findings highlight the effectiveness of integrating LMMs with traditional methods for pronunciation assessment, offering insights into the model's strengths and identifying areas for further improvement.

Related papers

• $C^2$AV-TSE: Context and Confidence-aware Audio Visual Target Speaker Extraction [80.57232374640911]
  We propose a model-agnostic strategy called the Mask-And-Recover (MAR) MAR integrates both inter- and intra-modality contextual correlations to enable global inference within extraction modules. To better target challenging parts within each sample, we introduce a Fine-grained Confidence Score (FCS) model.
  arXiv  Detail & Related papers  (2025-04-01T13:01:30Z)
• Enhancing LLM Evaluations: The Garbling Trick [0.0]
  Large language models (LLMs) become increasingly powerful, making it challenging to distinguish between models based on their performance. We propose a general method to transform existing LLM evaluations into a series of progressively more difficult tasks. Our results offer insights into the comparative reasoning abilities of these models, particularly highlighting distinctions between OpenAI's o1-preview and Google's gemini-pro-1.5.
  arXiv  Detail & Related papers  (2024-11-03T11:39:50Z)
• MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models [71.36392373876505]
  We introduce MMIE, a large-scale benchmark for evaluating interleaved multimodal comprehension and generation in Large Vision-Language Models (LVLMs) MMIE comprises 20K meticulously curated multimodal queries, spanning 3 categories, 12 fields, and 102 subfields, including mathematics, coding, physics, literature, health, and arts. It supports both interleaved inputs and outputs, offering a mix of multiple-choice and open-ended question formats to evaluate diverse competencies.
  arXiv  Detail & Related papers  (2024-10-14T04:15:00Z)
• LLaVA-Critic: Learning to Evaluate Multimodal Models [110.06665155812162]
  We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as a generalist evaluator.<n>LLaVA-Critic is trained using a high-quality critic instruction-following dataset that incorporates diverse evaluation criteria and scenarios.
  arXiv  Detail & Related papers  (2024-10-03T17:36:33Z)
• F-Eval: Assessing Fundamental Abilities with Refined Evaluation Methods [102.98899881389211]
  We propose F-Eval, a bilingual evaluation benchmark to evaluate the fundamental abilities, including expression, commonsense and logic. For reference-free subjective tasks, we devise new evaluation methods, serving as alternatives to scoring by API models.
  arXiv  Detail & Related papers  (2024-01-26T13:55:32Z)
• Silkie: Preference Distillation for Large Visual Language Models [56.10697821410489]
  This paper explores preference distillation for large vision language models (LVLMs) We first build a vision-language feedback dataset utilizing AI annotation. We adopt GPT-4V to assess the generated outputs regarding helpfulness, visual faithfulness, and ethical considerations. The resulting model Silkie, achieves 6.9% and 9.5% relative improvement on the MME benchmark regarding the perception and cognition capabilities.
  arXiv  Detail & Related papers  (2023-12-17T09:44:27Z)
• Positive and Risky Message Assessment for Music Products [9.545182238852545]
  We introduce a pioneering research challenge: evaluating positive and potentially harmful messages within music products. We introduce an efficient multi-task predictive model fortified with ordinality-enforcement to address this challenge.
  arXiv  Detail & Related papers  (2023-09-18T22:20:13Z)
• MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios [26.852744399985475]
  Pronunciation assessment models enable users to practice language skills in a manner similar to real-life communication. We propose MultiPA, a Multitask Pronunciation Assessment model that provides sentence-level accuracy, fluency, prosody, and word-level accuracy assessment for open responses.
  arXiv  Detail & Related papers  (2023-08-24T01:24:09Z)
• MMBench: Is Your Multi-modal Model an All-around Player? [114.45702807380415]
  We propose MMBench, a benchmark for assessing the multi-modal capabilities of vision-language models. MMBench is meticulously curated with well-designed quality control schemes. MMBench incorporates multiple-choice questions in both English and Chinese versions.
  arXiv  Detail & Related papers  (2023-07-12T16:23:09Z)
• ALL-IN-ONE: Multi-Task Learning BERT models for Evaluating Peer Assessments [2.544539499281093]
  This paper presents two MTL models for evaluating peer-review comments by leveraging the state-of-the-art pre-trained language representation models BERT and DistilBERT. Our results demonstrate that BERT-based models significantly outperform previous GloVe-based methods by around 6% in F1-score on tasks of detecting a single feature.
  arXiv  Detail & Related papers  (2021-10-08T05:13:41Z)

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.