Spectro-Temporal Modulation Representation Framework for Human-Imitated Speech Detection
Human-imitated speech poses a greater challenge than AI-generated speech for both human listeners and automatic detection systems. Unlike AI-generated speech, which often contains artifacts, over-smoothed spectra, or robotic cues, imitated speech is produced naturally by humans, thereby preserving a higher degree of naturalness that makes imitation-based speech forgery significantly more challenging to detect using conventional acoustic or cepstral features. To overcome this challenge, this study proposes an auditory perception-based Spectro-Temporal Modulation (STM) representation framework for human-imitated speech detection. The STM representations are derived from two cochlear filterb...
| Authors | Khalid Zaman, Masashi Unoki |
|---|---|
| Primary category | cs.SD |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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