  Existing fake audio detection systems perform well in in-domain testing, but
still face many challenges in out-of-domain testing. This is due to the
mismatch between the training and test data, as well as the poor
generalizability of features extracted from limited views. To address this, we
propose multi-view features for fake audio detection, which aim to capture more
generalized features from prosodic, pronunciation, and wav2vec dimensions.
Specifically, the phoneme duration features are extracted from a pre-trained
model based on a large amount of speech data. For the pronunciation features, a
Conformer-based phoneme recognition model is first trained, keeping the
acoustic encoder part as a deeply embedded feature extractor. Furthermore, the
prosodic and pronunciation features are fused with wav2vec features based on an
attention mechanism to improve the generalization of fake audio detection
models. Results show that the proposed approach achieves significant
performance gains in several cross-dataset experiments.
