AGenCi: Age and gender audio classification for forensic investigations of child sexual abuse and exploitation

Age and gender classification using speech signals is a critical task in digital forensics, with many important applications including investigations of child sexual abuse and exploitation (CSAE). While a large amount of research has explored various machine learning (ML) and deep learning (DL) techniques for this, challenges remain in accurately identifying children’s speech, especially in the context of detecting child sexual abuse material (CSAM). This is largely due to the lack of reliable/balanced datasets and the variability of children’s speech data. In this study, we present a framework based on OpenAI’s Whisper-medium model, integrating a custom multi-layer feedforward classification network to perform binary gender classification, binary age classification, and multi-class age classification. We call this framework AGenCi (Age and Gender audio Classification for forensic Investigations of CSAE). To fill the research gap and facilitate evaluation, three datasets are curated using well-known and publicly available speech corpora to address biases related to age, gender, and linguistic diversity. Several traditional ML models and recent DL models are selected in our benchmarking experiments. The results indicate that our framework achieves the highest accuracy of 95.6%, 94.5%, and 87.6% for the binary gender, binary age, and multi-age classification, respectively, consistently outperforming other models.