The Paradigm for Creating Multi-lingual Text-To-Speech Voice Databases

2006 ◽  
pp. 736-747 ◽  
Author(s):  
Min Chu ◽  
Yong Zhao ◽  
Yining Chen ◽  
Lijuan Wang ◽  
Frank Soong
Keyword(s):  
Text To Speech

A Review of 21 iPad Applications for Augmentative and Alternative Communication Purposes

2012 ◽  
Vol 21 (2) ◽  
pp. 60-71 ◽  
Author(s):  
Ashley Alliano ◽  
Kimberly Herriger ◽  
Anthony D. Koutsoftas ◽  
Theresa E. Bartolotta
Keyword(s):  
Augmentative And Alternative Communication ◽  
Cost Effective ◽  
Alternative Form ◽  
Alternative Communication ◽  
Text To Speech ◽  
Reference Guide ◽  
Expressive Communication ◽  
Communication Needs ◽  
User Friendly ◽  
Ipad Applications

Abstract Using the iPad tablet for Augmentative and Alternative Communication (AAC) purposes can facilitate many communicative needs, is cost-effective, and is socially acceptable. Many individuals with communication difficulties can use iPad applications (apps) to augment communication, provide an alternative form of communication, or target receptive and expressive language goals. In this paper, we will review a collection of iPad apps that can be used to address a variety of receptive and expressive communication needs. Based on recommendations from Gosnell, Costello, and Shane (2011), we describe the features of 21 apps that can serve as a reference guide for speech-language pathologists. We systematically identified 21 apps that use symbols only, symbols and text-to-speech, and text-to-speech only. We provide descriptions of the purpose of each app, along with the following feature descriptions: speech settings, representation, display, feedback features, rate enhancement, access, motor competencies, and cost. In this review, we describe these apps and how individuals with complex communication needs can use them for a variety of communication purposes and to target a variety of treatment goals. We present information in a user-friendly table format that clinicians can use as a reference guide.

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Acoustic parametric database design and optimization for Uyghur text-to-speech system

2014 ◽  
Author(s):  
Gulnar Ali ◽  
Askar Rozi ◽  
Akbar Pattar ◽  
Askar Hamdulla
Keyword(s):  
Database Design ◽  
Text To Speech ◽  
Design And Optimization

Text-to-speech for television. General requirements

2013 ◽  
Keyword(s):  
Text To Speech

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

Objective Evaluation Methods for Chinese Text-To-Speech Systems

2016 ◽  
Author(s):  
Teng Zhang ◽  
Zhipeng Chen ◽  
Ji Wu ◽  
Sam Lai ◽  
Wenhui Lei ◽  
...  
Keyword(s):  
Chinese Text ◽  
Objective Evaluation ◽  
Evaluation Methods ◽  
Text To Speech

Building Audio-Visual Phonetically Annotated Arabic Corpus for Expressive Text to Speech

2017 ◽  
Author(s):  
Omnia Abdo ◽  
Sherif Abdou ◽  
Mervat Fashal
Keyword(s):  
Text To Speech

Implementation of Concatenation Technique for Low Resource Text-To-Speech System Based on Marathi Talking Calculator

2018 ◽  
Author(s):  
Monica Mundada ◽  
Sangramsing Kayte ◽  
Pradip Das
Keyword(s):  
Text To Speech ◽  
Low Resource

Towards Universal Text-to-Speech

2020 ◽  
Author(s):  
Jingzhou Yang ◽  
Lei He
Keyword(s):  
Text To Speech

WikiSpeech – enabling open source text-to-speech for Wikipedia

2016 ◽  
Author(s):  
John Andersson ◽  
Sebastian Berlin ◽  
André Costa ◽  
Harald Berthelsen ◽  
Hanna Lindgren ◽  
...  
Keyword(s):  
Open Source ◽  
Text To Speech ◽  
Source Text
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