On the Detection of Pitch-Shifted Voice: Machines and Human Listeners

We present a performance comparison between human listeners and a simple algorithm for the task of speech anomaly detection. The algorithm utilises an intentionally small set of features derived from the source-filter model, with the aim of validating that key components of source-filter theory characterise how humans perceive anomalies. We furthermore recognise that humans are adept at detecting anomalies without prior exposure to a given anomaly class. To that end, we also consider the algorithm performance when operating via the principle of unsupervised learning where a null model is derived from normal speech recordings. We evaluate both the algorithm and human listeners for pitch-shift detection where the pitch of a speech sample is intentionally modified using software, a phenomenon of relevance to the fields of fraud detection and forensics. Our results show that humans can only detect pitch-shift reliably at more extreme levels, and that the performance of the algorithm matches closely with that of humans.