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

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arxiv 1903.11570 v1 pith:6QOQYGBO submitted 2019-03-27 cs.CL cs.AIcs.LGcs.SDeess.AS

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

No\'e Tits , Fengna Wang , Kevin El Haddad , Vincent Pagel , Thierry Dutoit This is my paper
classification cs.CL cs.AIcs.LGcs.SDeess.AS
keywords speechcontrollatentdifferentexpressiveinterpretationspacessynthesis
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The field of Text-to-Speech has experienced huge improvements last years benefiting from deep learning techniques. Producing realistic speech becomes possible now. As a consequence, the research on the control of the expressiveness, allowing to generate speech in different styles or manners, has attracted increasing attention lately. Systems able to control style have been developed and show impressive results. However the control parameters often consist of latent variables and remain complex to interpret. In this paper, we analyze and compare different latent spaces and obtain an interpretation of their influence on expressive speech. This will enable the possibility to build controllable speech synthesis systems with an understandable behaviour.

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  1. A Methodology for Controlling the Emotional Expressiveness in Synthetic Speech -- a Deep Learning approach

    eess.AS 2019-07 unverdicted novelty 3.0

    A methodology is proposed for emotional text-to-speech using emotional data collection, transfer-learning-based annotation of expressiveness features, and fine-tuning of a neutral TTS model.