使用最先进的语音模型评估加纳学生的朗读流畅度
Using State-of-the-Art Speech Models to Evaluate Oral Reading Fluency in Ghana
AI总结：
本文研究利用Whisper V2等大规模语音模型在加纳自动评估学生朗读流畅度，在真实教室数据上取得13.5的词错误率，且自动评分与专家评分相关系数达0.96，证明该方法在低资源、多语言教育环境中具有可行性和可扩展性。
摘要：
本文报告了一组三项近期实验，这些实验利用大规模语音模型评估加纳学生的朗读流畅度（ORF）。虽然ORF是衡量基础读写能力的一项成熟指标，但对其进行评估通常需要学生与受过培训的评估员进行一对一测试，这一过程耗时且成本高昂。自动化评估ORF可以支持更好的读写教学，尤其是在因班级规模大、资源有限而很少进行形成性评估的教育环境中。据我们所知，这项研究是首批考察最新版本的大规模语音模型（Whisper V2 wav2vec2.0）在全球南方国家用于ORF评估的研究之一。我们发现，Whisper V2对加纳学生朗读语音的转写词错误率（WER）为13.5。这一结果接近该模型在成人语音上的平均WER（12.8），若放在几年前，这一水平在儿童语音转写领域已可被视为最先进水平。我们还发现，当这些转写结果被用于生成全自动ORF评分时，其与人类专家评分员给出的分数高度一致，相关系数为0.96。重要的是，这些结果是在一个具有代表性的数据集上取得的（即学生带有地方口音，录音采集自真实教室环境），并且使用的是免费公开的现成语音模型（即未进行微调）。这表明，使用大规模语音模型评估ORF在资源较少、语言多样的教育环境中可能具有实施和推广的可行性。
英文摘要：
This paper reports on a set of three recent experiments utilizing large-scale speech models to evaluate the oral reading fluency (ORF) of students in Ghana. While ORF is a well-established measure of foundational literacy, assessing it typically requires one-on-one sessions between a student and a trained evaluator, a process that is time-consuming and costly. Automating the evaluation of ORF could support better literacy instruction, particularly in education contexts where formative assessment is uncommon due to large class sizes and limited resources. To our knowledge, this research is among the first to examine the use of the most recent versions of large-scale speech models (Whisper V2 wav2vec2.0) for ORF assessment in the Global South. We find that Whisper V2 produces transcriptions of Ghanaian students reading aloud with a Word Error Rate of 13.5. This is close to the model's average WER on adult speech (12.8) and would have been considered state-of-the-art for children's speech transcription only a few years ago. We also find that when these transcriptions are used to produce fully automated ORF scores, they closely align with scores generated by expert human graders, with a correlation coefficient of 0.96. Importantly, these results were achieved on a representative dataset (i.e., students with regional accents, recordings taken in actual classrooms), using a free and publicly available speech model out of the box (i.e., no fine-tuning). This suggests that using large-scale speech models to assess ORF may be feasible to implement and scale in lower-resource, linguistically diverse educational contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
