Abstract
Speech-based AI educational applications have gained significant interest in recent years, particularly for children. However, children speech research remains limited due to the lack of publicly available datasets, especially for low-resource languages such as this http URL paper presents Abjad-Kids, an Arabic speech dataset designed for kindergarten and primary education, focusing on fundamental learning of alphabets, numbers, and colors. The dataset consists of 46397 audio samples collected from children aged 3 - 12 years, covering 141 classes. All samples were recorded under controlled specifications to ensure consistency in duration, sampling rate, and format. To address high intra-class similarity among Arabic phonemes and the limited samples per class, we propose a hierarchical audio classification based on CNN-LSTM architectures. Our proposed methodology decomposes alphabet recognition into a two-stage process: an initial grouping classification model followed by specialized classifiers for each group. Both strategies: static linguistic-based grouping and dynamic clustering-based grouping, were evaluated. Experimental results demonstrate that static linguistic-based grouping achieves superior performance. Comparisons between traditional machine learning with deep learning approaches, highlight the effectiveness of CNN-LSTM models combined with data augmentation. Despite achieving promising results, most of our experiments indicate a challenge with overfitting, which is likely due to the limited number of samples, even after data augmentation and model regularization. Thus, future work may focus on collecting additional data to address this issue. Abjad-Kids will be publicly available. We hope that Abjad-Kids enrich children representation in speech dataset, and be a good resource for future research in Arabic speech classification for kids.
Abstract (translated)
基于语音的人工智能教育应用近年来备受关注，尤其针对儿童群体。然而，由于缺乏公开可用的数据集，儿童语音研究仍较为有限，特别是对于阿拉伯语这类低资源语言。本文提出Abjad-Kids——一个面向幼儿园及小学教育的阿拉伯语语音数据集，专注于字母、数字和颜色的基础学习。该数据集包含从3至12岁儿童收集的46397个音频样本，覆盖141个类别。所有样本均在受控规范下录制，以确保时长、采样率和格式的一致性。为应对阿拉伯语音素类内相似度高及每类样本有限的问题，我们提出了一种基于CNN-LSTM架构的分层音频分类方法。所提方法将字母识别分解为两阶段过程：首先进行分组分类，随后对每个组别采用专用分类器。两种策略——基于静态语言学的分组与基于动态聚类的分组——均经过评估。实验结果表明，基于静态语言学的分组表现更优。通过对比传统机器学习与深度学习方法，凸显了结合数据增强的CNN-LSTM模型的有效性。尽管取得了令人鼓舞的结果，但多数实验仍表明存在过拟合挑战，这很可能源于样本数量有限，即便经过数据增强和模型正则化处理。因此，未来工作可聚焦于收集更多数据以解决此问题。Abjad-Kids将公开提供。我们希望该数据集能丰富儿童语音数据集的代表性，并为阿拉伯语儿童语音分类的后续研究提供优质资源。
URL
https://arxiv.org/abs/2603.20255
