DEEPA: A Deep Neural Analyzer for Speech and Singing Vocoding

@article{Nikonorov2021DEEPAAD,
  title={DEEPA: A Deep Neural Analyzer for Speech and Singing Vocoding},
  author={Sergey Nikonorov and Berrak Sisman and Mingyang Zhang and Haizhou Li},
  journal={2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
  year={2021},
  pages={618-625}
}
Conventional vocoders are commonly used as analysis tools to provide interpretable features for downstream tasks such as speech synthesis and voice conversion. They are built under certain assumptions about the signals following signal processing principle, therefore, not easily generalizable to different audio, for example, from speech to singing. In this paper, we propose a deep neural analyzer, denoted as DeepA – a neural vocoder that extracts F0 and timbre/aperiodicity encoding from the… 

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