论文标题
探索不使用生物学/心理动机的神经网络模块的TT（Zerospeech 2020）
Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020)
论文作者
论文摘要
在这项研究中，我们报道了我们对2020年零资源演讲挑战中没有文本的文本到语音的探索，参与者提出了一个端到端，无监督的系统，该系统一起学习了语音识别和TT。我们使用人工神经网络（ANN）的生物学/心理动机模块（ANN）解决了挑战，对将人类语言作为生物学/心理问题的无监督学习特别感兴趣。该系统首先处理具有回声网络（ESN）的Mel频率CEPSTRAL系数（MFCC）帧，并模拟皮质微电路中的计算。我们的原始变异自动编码器（VAE）离散了结果，该杂交自动编码器（VAE）实现了基于迪利奇的贝叶斯聚类在计算语言学和认知科学中广泛接受的。然后，通过神经网络实现源滤波器模型以进行语音生产，将离散的信号恢复为声波。
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.
