Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020)

By Takashi Morita, Hiroki Koda. 2020

8 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

Citations in the corpus

• On Generative Spoken Language Modeling from Raw Audio (2021, neutral): “…unsupervised manner, has attracted attention from both the machine learning and the speech communities (Liu et al., 2019a; Feng et al., 2019; Nayak et al., 2019; Tjan-dra et al., 2019; Schneider et al., 2019; Baevski et al., 2020a; Chen and Hain, 2020; Morita and Koda, 2020; Tobing et al., 2020).”