标题： 端到端语音评估模型在NOCASA 2025挑战赛中的比较 显示英文标题

标题： Comparison of End-to-end Speech Assessment Models for the NOCASA 2025 Challenge

摘要： 本文介绍了针对NOCASA 2025挑战赛开发的三种端到端模型的分析，旨在对学习挪威语作为第二语言的儿童进行自动逐词发音评估。 我们的模型包括一种编码器-解码器孪生架构（E2E-R）、一种利用预训练wav2vec2.0表示的前缀调优直接分类模型，以及一种新颖的模型，该模型通过CTC整合无对齐发音质量（GOP）特征。 我们引入了一种加权序数交叉熵损失，专门用于优化诸如未加权平均召回率和平均绝对误差等指标。 在所探索的方法中，基于GOP-CTC的模型表现最佳，显著超越了挑战基线，并取得了顶级排行榜分数。 显示英文摘要

摘要： This paper presents an analysis of three end-to-end models developed for the NOCASA 2025 Challenge, aimed at automatic word-level pronunciation assessment for children learning Norwegian as a second language. Our models include an encoder-decoder Siamese architecture (E2E-R), a prefix-tuned direct classification model leveraging pretrained wav2vec2.0 representations, and a novel model integrating alignment-free goodness-of-pronunciation (GOP) features computed via CTC. We introduce a weighted ordinal cross-entropy loss tailored for optimizing metrics such as unweighted average recall and mean absolute error. Among the explored methods, our GOP-CTC-based model achieved the highest performance, substantially surpassing challenge baselines and attaining top leaderboard scores.