9 citations · 11 across the 4 of their papers we have counts for
6 papers · 1 filter
Low Bitrate High-Quality RVQGAN-based Discrete Speech Tokenizer
Slava Shechtman, Avihu Dekel
Discrete Audio codecs (or audio tokenizers) have recently regained interest due to the ability of Large Language Models (LLMs) to learn their compressed acoustic representations. V…
Speech Synthesis From Continuous Features Using Per-Token Latent Diffusion
Arnon Turetzky, Avihu Dekel, Nimrod Shabtay +5
We present SALAD, a zero-shot TTS autoregressive model operating over continuous speech representations. SALAD utilizes a per-token diffusion process to refine and predict continuo…
Supervised and Unsupervised Approaches for Controlling Narrow Lexical Focus in Sequence-to-Sequence Speech Synthesis
Slava Shechtman, Raul Fernandez, David Haws
Although Sequence-to-Sequence (S2S) architectures have become state-of-the-art in speech synthesis, capable of generating outputs that approach the perceptual quality of natural sa…
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…
Sequence to Sequence Neural Speech Synthesis with Prosody Modification Capabilities
Slava Shechtman, Alex Sorin
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…
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…