_:n090fbcc05548466cada01526e43df90dxb11
<http://schema.org/description>
"Mel-frequency filter bank (MFB) based approaches have the advantage of\nlearning speech compared to raw spectrum since MFB has less feature size.\nHowever, speech generator with MFB approaches require additional vocoder that\nneeds a huge amount of computation expense for training process. The additional\npre/post processing such as MFB and vocoder is not essential to convert real\nhuman speech to others. It is possible to only use the raw spectrum along with\nthe phase to generate different style of voices with clear pronunciation. In\nthis regard, we propose a fast and effective approach to convert realistic\nvoices using raw spectrum in a parallel manner. Our transformer-based model\narchitecture which does not have any CNN or RNN layers has shown the advantage\nof learning fast and solved the limitation of sequential computation of\nconventional RNN. In this paper, we introduce a vocoder-free end-to-end voice\nconversion method using transformer network. The presented conversion model can\nalso be used in speaker adaptation for speech recognition. Our approach can\nconvert the source voice to a target voice without using MFB and vocoder. We\ncan get an adapted MFB for speech recognition by multiplying the converted\nmagnitude with phase. We perform our voice conversion experiments on TIDIGITS\ndataset using the metrics such as naturalness, similarity, and clarity with\nmean opinion score, respectively."
<https://deepai.org/profile/june-woo-kim>