Methods for detecting these manipulations should be characterized by good generalization and stability leading to robustness against attacks conducted with techniques that are not explicitly included in the training. The results of objective and subjective evaluations show the effectiveness of our method.Īudio DeepFakes allow the creation of high-quality, convincing utterances and therefore pose a threat due to its potential applications such as impersonation or fake news. We propose an automatic conversion method for conventional labels and show how to automatically reproduce pitch accents and phonemes.
The proposed method does not need the boundary positions of phonemes. By contrast, the labels of conventional statistical parametric speech synthesis methods are complicated, and the cost of time alignments such as aligning the boundaries of phonemes is high.
The proposed phonetic and prosodic labels have simple descriptions and a low production cost. accents, pauses, accent breaks, and sentence endings, in several seq2seq AM methods. The method involves inserting a sequence of prosodic symbols between phonetic symbols that are then used to reproduce prosodic acoustic features, i.e. This paper describes a method to control prosodic features using phonetic and prosodic symbols as input of attention-based sequence-to-sequence (seq2seq) acoustic modeling (AM) for neural text-to-speech (TTS).
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