speech-synthesis

Expressive Speech Synthesis

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysis

Kevin El Haddad, T. Dutoit, Noé Tits, Fengna Wang, Vincent Pagel•Tue Mar 26 2019

This paper analyzes and compares different latent spaces and obtains an interpretation of their influence on expressive speech to enable the possibility to build controllable speech synthesis systems with an understandable behaviour.

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Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron

Eric Battenberg, R. Saurous, Ron J. Weiss, R. Clark, Yuxuan Wang, Joel Shor, R. Skerry-Ryan, Daisy Stanton, Y. Xiao•Fri Mar 23 2018

An extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing the desired prosody results in synthesized audio that matches the prosody of the reference signal with fine time detail.

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Exploring Transfer Learning for Low Resource Emotional TTS

Kevin El Haddad, T. Dutoit, Noé Tits•Sun Jan 13 2019

This paper investigates how to leverage fine-tuning on a pre-trained Deep Learning-based TTS model to synthesize speech with a small dataset of another speaker, and adapts this model to have emotional TTS by fine- Tuning the neutral T TS model with asmall emotional dataset.

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Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech

Pengfei Wu, Junjie Pan, Chenchang Xu, Junhui Zhang, Lin Wu, Xiang Yin, Zejun Ma•Thu Oct 07 2021

Experimental results show that the proposed cross-speaker emotion transfer method outperforms the multi-reference based baseline in terms of timbre similarity, stability and emotion perceive evaluations.

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Robust and Fine-grained Prosody Control of End-to-end Speech Synthesis

Younggun Lee, Taesu Kim•Mon Nov 05 2018

The proposed methods introduce temporal structures in the embedding networks, thus enabling fine-grained control of the speaking style of the synthesized speech and introducing the temporal normalization of prosody embeddings, which shows better robustness against speaker perturbations during prosody transfer tasks.

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