Abstract
This study presented the development of a deep learning-based Automatic Speech Recognition system for Shona, a low-resource language characterized by unique tonal and grammatical complexities. The research aimed to address the challenges posed by limited training data, lack of labelled data, and the intricate tonal nuances present in Shona speech, with the objective of achieving significant improvements in recognition accuracy compared to traditional statistical models. The research first explored the feasibility of using deep learning to develop an accurate ASR system for Shona. Second, it investigated the specific challenges involved in designing and implementing deep learning architectures for Shona speech recognition and proposed strategies to mitigate these challenges. Lastly, it compared the performance of the deep learning-based model with existing statistical models in terms of accuracy. The developed ASR system utilized a hybrid architecture consisting of a Convolutional Neural Network for acoustic modelling and a Long Short-Term Memory network for language modelling. To overcome the scarcity of data, data augmentation techniques and transfer learning were employed. Attention mechanisms were also incorporated to accommodate the tonal nature of Shona speech. The resulting ASR system achieved impressive results, with a Word Error Rate of 29%, Phoneme Error Rate of 12%, and an overall accuracy of 74%. These metrics indicated the potential of deep learning to enhance ASR accuracy for under-resourced languages like Shona. This study contributed to the advancement of ASR technology for under-resourced languages like Shona, ultimately fostering improved accessibility and communication for Shona speakers worldwide.
Abstract (translated)
这项研究开发了一种基于深度学习的自动语音识别（ASR）系统，用于处理资源较少且具有独特音调和语法复杂性的绍纳语。该研究旨在通过使用深度学习克服训练数据有限、标签数据缺乏以及绍纳语特有的音调细微差别所带来的挑战，并实现与传统统计模型相比显著提高的识别准确性。具体来说，这项研究做了三件事： 1. 探索了利用深度学习为绍纳语开发准确ASR系统的可行性； 2. 研究了在设计和实施用于绍纳语音识别的深度学习架构时所面临的特定挑战，并提出了缓解这些挑战的战略方法； 3. 比较了基于深度学习模型与现有统计模型在准确性方面的表现。 该研究中开发的ASR系统采用了混合架构，其中包括用于声学建模的卷积神经网络（CNN）和用于语言建模的长短期记忆网络（LSTM）。为了解决数据稀缺的问题，研究人员采用了一系列的数据增强技术和迁移学习方法。此外，为了适应绍纳语特有的音调特征，还融入了注意力机制。 经过这些技术的应用，该ASR系统取得了显著成果：单词错误率为29%，音素错误率为12%，整体准确率达到了74%。这些指标表明深度学习在提高像绍纳这样的资源较少语言的自动语音识别准确性方面具有巨大潜力。 这项研究为推进用于绍纳及其他资源较少语言的自动语音识别技术做出了贡献，最终有助于提升全球绍纳语使用者之间的可访问性和交流效率。
URL
https://arxiv.org/abs/2507.21331
