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The present paper proposes a waveform boundary detection system for audiospoofing attacks containing partially manipulated segments. Partiallyspoofed/fake audio, where part of the utterance is replaced, either withsynthetic or natural audio clips, has recently been reported as one scenario ofaudio deepfakes. As deepfakes can be a threat to social security, the detectionof such spoofing audio is essential. Accordingly, we propose to address theproblem with a deep learning-based frame-level detection system that can detectpartially spoofed audio and locate the manipulated pieces. Our proposed methodis trained and evaluated on data provided by the ADD2022 Challenge. We evaluateour detection model concerning various acoustic features and networkconfigurations. As a result, our detection system achieves an equal error rate(EER) of 6.58% on the ADD2022 challenge test set, which is the best performancein partially spoofed audio detection systems that can locate manipulated clips.

Waveform Boundary Detection for Partially Spoofed Audio