activity
20192022
most citedDynamic Prosody Generation for Speech Synthesis using Linguistics-Driven Acoustic Embedding Selection

30 citations · 35 across the 4 of their papers we have counts for

collaborators

5 papers

eess.AS2022

Analysis and Utilization of Entrainment on Acoustic and Emotion Features in User-agent Dialogue

Daxin Tan, Nikos Kargas, David McHardy +6

Entrainment is the phenomenon by which an interlocutor adapts their speaking style to align with their partner in conversations. It has been found in different dimensions as acoust…

cs.SD2021★ 1 cited

Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech

Marek Strong, Jonas Rohnke, Antonio Bonafonte +2

We present a Split Vector Quantized Variational Autoencoder (SVQ-VAE) architecture using a split vector quantizer for NTTS, as an enhancement to the well-known Variational Autoenco…

eess.AS2020★ 4 cited

Parallel WaveNet conditioned on VAE latent vectors

Jonas Rohnke, Tom Merritt, Jaime Lorenzo-Trueba +4

Recently the state-of-the-art text-to-speech synthesis systems have shifted to a two-model approach: a sequence-to-sequence model to predict a representation of speech (typically m…

cs.CL2019★ 30 cited

Dynamic Prosody Generation for Speech Synthesis using Linguistics-Driven Acoustic Embedding Selection

Shubhi Tyagi, Marco Nicolis, Jonas Rohnke +2

Recent advances in Text-to-Speech (TTS) have improved quality and naturalness to near-human capabilities when considering isolated sentences. But something which is still lacking i…

eess.AS2019

Fine-grained robust prosody transfer for single-speaker neural text-to-speech

Viacheslav Klimkov, Srikanth Ronanki, Jonas Rohnke +1

We present a neural text-to-speech system for fine-grained prosody transfer from one speaker to another. Conventional approaches for end-to-end prosody transfer typically use eithe…