Journal ArticleDOI
Prosody modeling for syllable based text-to-speech synthesis using feedforward neural networks
V. Ramu Reddy,K. Sreenivasa Rao +1 more
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TLDR
Feature related to the linguistic and the production constraints are proposed for modeling the prosodic parameters such as duration, intonation and intensities of the syllables for text-to-speech synthesis (TTS) system.About:
This article is published in Neurocomputing.The article was published on 2016-01-01. It has received 19 citations till now. The article focuses on the topics: Prosody & Feedforward neural network.read more
Citations
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Journal ArticleDOI
Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet
Journal ArticleDOI
An efficient adaptive artificial neural network based text to speech synthesizer for Hindi language
TL;DR: In this article, a text to speech synthesizer for the Hindi language is proposed, which relies on the coefficients of Mel-frequency cepstral (MFCC) features extracted to the production and linguistic constraints proposed for modeling the parameters such as intonation, duration, and syllable intensities.
Journal ArticleDOI
The Effects of Modulating Fundamental Frequency and Speech Rate on the Intelligibility, Communication Efficiency, and Perceived Naturalness of Synthetic Speech.
TL;DR: Sentence-level f0 variation increased naturalness ratings of synthesized speech, whether the variation was prosodically natural or not, which may impact future speech synthesis designs.
Proceedings ArticleDOI
Mongolian prosodic phrase prediction using suffix segmentation
TL;DR: The experimental results show that the proposed method has significantly enhanced the performance of the Mongolian prosodic phrase prediction system through comparing with the conventional method that treats Mongolian word as token directly.
Journal ArticleDOI
Phone-Level Prosody Modelling With GMM-Based MDN for Diverse and Controllable Speech Synthesis
Chenpeng Du,Kai Yu +1 more
TL;DR: This work proposes a novel approach that models phone-level prosodies with a GMM-based mixture density network(MDN) and then extends it for multi-speaker TTS using speaker adaptation transforms of Gaussian means and variances and shows that it can clone the prosodies from a reference speech by sampling prosody from the Gaussian components that produce the reference prosodies.
References
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TL;DR: artificial neural networks, artificial neural networks , مرکز فناوری اطلاعات و اصاع رسانی, کδاوρزی
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TL;DR: In this article, the authors present a survey of Indian cities in terms of Latitude (°) +N/-SLongitude (−) +E/-W New Delhi India C V Raman Science Club 28.616 77.195
Related Papers (5)
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