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State-of-the-art sequence-to-sequence acoustic networks, that convert aphonetic sequence to a sequence of spectral features with no explicit prosodyprediction, generate speech with close to natural quality, when cascaded withneural vocoders, such as Wavenet. However, the combined system is typically tooheavy for real-time speech synthesis on a CPU. In this work we present asequence-to-sequence acoustic network combined with lightweight LPCNet neuralvocoder, designed for real-time speech synthesis on a CPU. In addition, thesystem allows sentence-level pace and expressivity control at inference time.We demonstrate that the proposed system can synthesize high quality 22 kHzspeech in real-time on a general-purpose CPU. In terms of MOS score degradationrelative to PCM, the system attained as low as 6.1-6.5% for quality and 6.3-7.0% for expressiveness, reaching equivalent or better quality when compared toa similar system with a Wavenet vocoder backend.

Controllable Sequence-To-Sequence Neural TTS with LPCNET Backend for Real-time Speech Synthesis on CPU