  For articulatory-to-acoustic mapping using deep neural networks, typically
spectral and excitation parameters of vocoders have been used as the training
targets. However, vocoding often results in buzzy and muffled final speech
quality. Therefore, in this paper on ultrasound-based articulatory-to-acoustic
conversion, we use a flow-based neural vocoder (WaveGlow) pre-trained on a
large amount of English and Hungarian speech data. The inputs of the
convolutional neural network are ultrasound tongue images. The training target
is the 80-dimensional mel-spectrogram, which results in a finer detailed
spectral representation than the previously used 25-dimensional Mel-Generalized
Cepstrum. From the output of the ultrasound-to-mel-spectrogram prediction,
WaveGlow inference results in synthesized speech. We compare the proposed
WaveGlow-based system with a continuous vocoder which does not use strict
voiced/unvoiced decision when predicting F0. The results demonstrate that
during the articulatory-to-acoustic mapping experiments, the WaveGlow neural
vocoder produces significantly more natural synthesized speech than the
baseline system. Besides, the advantage of WaveGlow is that F0 is included in
the mel-spectrogram representation, and it is not necessary to predict the
excitation separately.
