  In this study, we reported our exploration of Text-To-Speech without Text
(TTS without T) in the Zero Resource Speech Challenge 2020, in which
participants proposed an end-to-end, unsupervised system that learned speech
recognition and TTS together. We addressed the challenge using
biologically/psychologically motivated modules of Artificial Neural Networks
(ANN), with a particular interest in unsupervised learning of human language as
a biological/psychological problem. The system first processes Mel Frequency
Cepstral Coefficient (MFCC) frames with an Echo-State Network (ESN), and
simulates computations in cortical microcircuits. The outcome is discretized by
our original Variational Autoencoder (VAE) that implements the Dirichlet-based
Bayesian clustering widely accepted in computational linguistics and cognitive
science. The discretized signal is then reverted into sound waveform via a
neural-network implementation of the source-filter model for speech production.
