scholarly journals Can online translators and their speech capabilities help English learners improve their pronunciation?

2021 ◽  
pp. 126-131
Author(s):  
Yue He ◽  
Walcir Cardoso
Keyword(s):  
Speech Recognition ◽  
Phonological Awareness ◽  
English Learners ◽  
Speech Synthesis ◽  
Text To Speech ◽  
Efl Learners ◽  
Oral Production ◽  
Text To Speech Synthesis ◽  
Speech Features ◽  
Online Translators

This study investigated whether a translation tool (Microsoft Translator – MT) and its built-in speech features (Text-To-Speech synthesis – TTS – and speech recognition) can promote learners’ acquisition in pronunciation of English regular past tense -ed in a self-directed manner. Following a pretest/posttest design, we compared 29 participants’ performances of past -ed allomorphy (/t/, /d/, and /id/) by assessing their pronunciation in terms of phonological awareness, phonemic discrimination, and oral production. The findings highlight the affordances of MT regarding its pedagogical use for helping English as a Foreign Language (EFL) learners improve their pronunciation.

scholarly journals Language To Language Translation System

2020 ◽  
pp. 289-293
Author(s):  
Ms Pratheeksha ◽  
Pratheeksha Rai ◽  
Ms Vijetha
Keyword(s):  
Speech Recognition ◽  
Machine Translation ◽  
Automatic Speech Recognition ◽  
Speech Synthesis ◽  
Language Translation ◽  
Target Language ◽  
Translation System ◽  
Text To Speech ◽  
Source Language ◽  
Text To Speech Synthesis

The system used in Language to Language Translation is the phrases spoken in one language are immediately spoken in other language by the device. Language to Language Translation is a three steps software process which includes Automatic Speech Recognition, Machine Translation and Voice Synthesis. Language to Language system includes the major speech translation projects using different approaches for Speech Recognition, Translation and Text to Speech synthesis highlighting the major pros and cons for the approach being used. Language translation is a process that takes the conversational phrase in one language as an input and translated speech phrases in another language as the output. The three components of language-to-language translation are connected in a sequential order. Automatic Speech Recognition (ASR) is responsible for converting the spoken phrases of source language to the text in the same language followed by machine translation which translates the source language to next target language text and finally the speech synthesizer is responsible for text to speech conversion of target language.

Operational and experimental French telecommunication services using CNET speech recognition and text-to-speech synthesis

1995 ◽  
Vol 17 (3-4) ◽  
pp. 273-286 ◽  
Author(s):  
C. Sorin ◽  
D. Jouvet ◽  
C. Gagnoulet ◽  
D. Dubois ◽  
D. Sadek ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Speech Synthesis ◽  
Text To Speech ◽  
Telecommunication Services ◽  
Text To Speech Synthesis

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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