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20192022
most citedControllable Sequence-To-Sequence Neural TTS with LPCNET Backend for Real-time Speech Synthesis on CPU

2 citations · 2 across the 2 of their papers we have counts for

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eess.AS2020★ 2 cited

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

Slava Shechtman, Carmel Rabinovitz, Alex Sorin +2

State-of-the-art sequence-to-sequence acoustic networks, that convert a phonetic sequence to a sequence of spectral features with no explicit prosody prediction, generate speech wi…

eess.AS2019

High quality, lightweight and adaptable TTS using LPCNet

Zvi Kons, Slava Shechtman, Alex Sorin +2

We present a lightweight adaptable neural TTS system with high quality output. The system is composed of three separate neural network blocks: prosody prediction, acoustic feature…