Sequence to Sequence Neural Speech Synthesis with Prosody Modification Capabilities

Abstract

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.