Advancements in technology and the increased use of digital data threaten individual privacy, especially in speech containing Personally Identifiable Information (PII). Therefore, systems that can remove or process privacy-sensitive data in speech are needed, particularly for low-resource transcripts. These transcripts are minimally annotated or labeled automatically, which is less precise than human annotation. However, using them can simplify the development of de-identification systems in any language. In this study, we develop and evaluate an efficient speech de-identification system. We create an Indonesian speech dataset containing sensitive private information and design a system with three main components: speech recognition, information extraction, and masking. To enhance performance in low-resource settings, we incorporate transcription data in training, use data augmentation, and apply weakly supervised learning. Our results show that our techniques significantly improve privacy detection performance, with approximately 29% increase in F1 score, 20% in precision, and 30% in recall with minimally labeled data.