4 research outputs found
Sequence to Sequence Neural Speech Synthesis with Prosody Modification Capabilities
Modern sequence to sequence neural TTS systems provide close to natural
speech quality. Such systems usually comprise a network converting
linguistic/phonetic features sequence to an acoustic features sequence,
cascaded with a neural vocoder. The generated speech prosody (i.e. phoneme
durations, pitch and loudness) is implicitly present in the acoustic features,
being mixed with spectral information. Although the speech sounds natural, its
prosody realization is randomly chosen and cannot be easily altered. The
prosody control becomes an even more difficult task if no prosodic labeling is
present in the training data. Recently, much progress has been achieved in
unsupervised speaking style learning and generation, however human inspection
is still required after the training for discovery and interpretation of the
speaking styles learned by the system. In this work we introduce a fully
automatic method that makes the system aware of the prosody and enables
sentence-wise speaking pace and expressiveness control on a continuous scale.
While being useful by itself in many applications, the proposed prosody control
can also improve the overall quality and expressiveness of the synthesized
speech, as demonstrated by subjective listening evaluations. We also propose a
novel augmented attention mechanism, that facilitates better pace control
sensitivity and faster attention convergence.Comment: published at 10th ISCA Speech Synthesis Workshop (SSW-10, 2019