  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 input speech that emulate those
defined in conventional vocoders. Therefore, the resulting parameters are more
interpretable than other latent neural representations. At the same time, as
the deep neural analyzer is learnable, it is expected to be more accurate for
signal reconstruction and manipulation, and generalizable from speech to
singing. The proposed neural analyzer is built based on a variational
autoencoder (VAE) architecture. We show that DeepA improves F0 estimation over
the conventional vocoder (WORLD). To our best knowledge, this is the first
study dedicated to the development of a neural framework for extracting
learnable vocoder-like parameters.
