  The present paper proposes a waveform boundary detection system for audio
spoofing attacks containing partially manipulated segments. Partially
spoofed/fake audio, where part of the utterance is replaced, either with
synthetic or natural audio clips, has recently been reported as one scenario of
audio deepfakes. As deepfakes can be a threat to social security, the detection
of such spoofing audio is essential. Accordingly, we propose to address the
problem with a deep learning-based frame-level detection system that can detect
partially spoofed audio and locate the manipulated pieces. Our proposed method
is trained and evaluated on data provided by the ADD2022 Challenge. We evaluate
our detection model concerning various acoustic features and network
configurations. 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 performance
in partially spoofed audio detection systems that can locate manipulated clips.
