Deep Syntactic Analysis and Rule Based Accentuation in Text-to-Speech Synthesis

2008 ◽  
pp. 535-542 ◽  
Author(s):  
Antti Suni ◽  
Martti Vainio
Keyword(s):  
Speech Synthesis ◽  
Syntactic Analysis ◽  
Text To Speech ◽  
Rule Based ◽  
Text To Speech Synthesis

A rule-based phrase parser for real-time text-to-speech synthesis

1995 ◽  
Vol 1 (2) ◽  
pp. 191-212 ◽  
Author(s):  
Joan Bachenko ◽  
Eileen Fitzpatrick ◽  
Jeffrey Daugherty
Keyword(s):  
Real Time ◽  
Hard Of Hearing ◽  
Speech Synthesis ◽  
Break Point ◽  
Linguistic Context ◽  
Text To Speech ◽  
Rule Based ◽  
Front End ◽  
Text To Speech Synthesis ◽  
Break Points

AbstractText-to-speech systems are currently designed to work on complete sentences and paragraphs, thereby allowing front end processors access to large amounts of linguistic context. Problems with this design arise when applications require text to be synthesized in near real time, as it is being typed. How does the system decide which incoming words should be collected and synthesized as a group when prior and subsequent word groups are unknown? We describe a rule-based parser that uses a three cell buffer and phrasing rules to identify break points for incoming text. Words up to the break point are synthesized as new text is moved into the buffer; no hierarchical structure is built beyond the lexical level. The parser was developed for use in a system that synthesizes written telecommunications by Deaf and hard of hearing people. These are texts written entirely in upper case, with little or no punctuation, and using a nonstandard variety of English (e.g. WHEN DO I WILL CALL BACK YOU). The parser performed well in a three month field trial utilizing tens of thousands of texts. Laboratory tests indicate that the parser exhibited a low error rate when compared with a human reader.

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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