scholarly journals Text-To-Speech Synthesis Using Transfer Learning

2021 ◽  
pp. 139-144
Author(s):  
Ishita Satija ◽  
Vina Lomte ◽  
Yash Wani ◽  
Digisha Kaneria ◽  
Shubham Yadav
Keyword(s):  
Transfer Learning ◽  
Speech Synthesis ◽  
Text To Speech ◽  
Neural Organization ◽  
Proposed Model ◽  
Backward Wave ◽  
Text To Speech Synthesis ◽  
The Voice

We portray a neural organization based framework for text-to-speech (TTS) combination that can create discourse sound in the voice of various speakers, including those concealed during preparation. Our framework comprises of three autonomously prepared parts: (1) a speaker encoder network; (2) a grouping to-succession union organization based on Tacotron 2; (3) an auto-backward Wave Net-based vocoder network. We illustrate that the proposed model can move the information on speaker fluctuation learned by the discriminatively-prepared speaker encoder to the multi speaker TTS task, and can incorporate normal discourse from speakers concealed during preparation. We measure the significance of preparing the speaker encoder on a huge and different speaker set to acquire the best speculation execution. At last, we show that haphazardly inspected speaker embeddings can be utilized to integrate discourse in the voice of novel speakers divergent from those utilized in preparing, showing that the model has taken in a top-notch speaker portrayal.

scholarly journals Generating the Voice of the Interactive Virtual Assistant

2021 ◽  
Author(s):  
Adriana Stan ◽  
Beáta Lőrincz
Keyword(s):  
Speech Synthesis ◽  
Text Processing ◽  
Research Field ◽  
Text To Speech ◽  
Rule Based ◽  
Acoustic Modelling ◽  
Research Problems ◽  
Text To Speech Synthesis ◽  
Main Components ◽  
The Voice

This chapter introduces an overview of the current approaches for generating spoken content using text-to-speech synthesis (TTS) systems, and thus the voice of an Interactive Virtual Assistant (IVA). The overview builds upon the issues which make spoken content generation a non-trivial task, and introduces the two main components of a TTS system: text processing and acoustic modelling. It then focuses on providing the reader with the minimally required scientific details of the terminology and methods involved in speech synthesis, yet with sufficient knowledge so as to be able to make the initial decisions regarding the choice of technology for the vocal identity of the IVA. The speech synthesis methodologies’ description begins with the basic, easy to run, low-requirement rule-based synthesis, and ends up within the state-of-the-art deep learning landscape. To bring this extremely complex and extensive research field closer to commercial deployment, an extensive indexing of the readily and freely available resources and tools required to build a TTS system is provided. Quality evaluation methods and open research problems are, as well, highlighted at end of the chapter.

Integrating Articulatory Information in Deep Learning-Based Text-to-Speech Synthesis

2017 ◽  
Author(s):  
Beiming Cao ◽  
Myungjong Kim ◽  
Jan van Santen ◽  
Ted Mau ◽  
Jun Wang
Keyword(s):  
Deep Learning ◽  
Speech Synthesis ◽  
Text To Speech ◽  
Text To Speech Synthesis

Subset Selection, Adaptation, Gemination and Prosody Prediction for Amharic Text-to-Speech Synthesis

2019 ◽  
Author(s):  
Elshadai Tesfaye Biru ◽  
Yishak Tofik Mohammed ◽  
David Tofu ◽  
Erica Cooper ◽  
Julia Hirschberg
Keyword(s):  
Speech Synthesis ◽  
Subset Selection ◽  
Text To Speech ◽  
Text To Speech Synthesis ◽  
Prosody Prediction
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