  Recent advances in neural text-to-speech research have been dominated by
two-stage pipelines utilizing low-level intermediate speech representation such
as mel-spectrograms. However, such predetermined features are fundamentally
limited, because they do not allow to exploit the full potential of a
data-driven approach through learning hidden representations. For this reason,
several end-to-end methods have been proposed. However, such models are harder
to train and require a large number of high-quality recordings with
transcriptions. Here, we propose WavThruVec - a two-stage architecture that
resolves the bottleneck by using high-dimensional Wav2Vec 2.0 embeddings as
intermediate speech representation. Since these hidden activations provide
high-level linguistic features, they are more robust to noise. That allows us
to utilize annotated speech datasets of a lower quality to train the
first-stage module. At the same time, the second-stage component can be trained
on large-scale untranscribed audio corpora, as Wav2Vec 2.0 embeddings are
already time-aligned. This results in an increased generalization capability to
out-of-vocabulary words, as well as to a better generalization to unseen
speakers. We show that the proposed model not only matches the quality of
state-of-the-art neural models, but also presents useful properties enabling
tasks like voice conversion or zero-shot synthesis.
