Emotional Speech Datasets for English Speech Synthesis Purpose: A Review

2019 ◽  
pp. 61-66
Author(s):  
Noé Tits ◽  
Kevin El Haddad ◽  
Thierry Dutoit
Keyword(s):  
Speech Synthesis ◽  
Emotional Speech

scholarly journals HMM-Based Emotional Speech Synthesis Using Average Emotion Model

2006 ◽  
pp. 233-240 ◽  
Author(s):  
Long Qin ◽  
Zhen-Hua Ling ◽  
Yi-Jian Wu ◽  
Bu-Fan Zhang ◽  
Ren-Hua Wang
Keyword(s):  
Speech Synthesis ◽  
Emotional Speech ◽  
Emotion Model

Speech Synthesis of Emotions Using Vowel Features

2013 ◽  
Vol 1 (1) ◽  
pp. 54-67
Author(s):  
Kanu Boku ◽  
Taro Asada ◽  
Yasunari Yoshitomi ◽  
Masayoshi Tabuse
Keyword(s):  
Fundamental Frequency ◽  
Speech Synthesis ◽  
Male Subject ◽  
Maximum Amplitude ◽  
Synthetic Speech ◽  
Emotional Speech ◽  
Prosodic Features ◽  
Initial Investigation ◽  
Synthesis Research ◽  
Case Based

Recently, methods for adding emotion to synthetic speech have received considerable attention in the field of speech synthesis research. For generating emotional synthetic speech, it is necessary to control the prosodic features of the utterances. The authors propose a case-based method for generating emotional synthetic speech by exploiting the characteristics of the maximum amplitude and the utterance time of vowels, and the fundamental frequency of emotional speech. As an initial investigation, they adopted the utterance of Japanese names, which are semantically neutral. By using the proposed method, emotional synthetic speech made from the emotional speech of one male subject was discriminable with a mean accuracy of 70% when ten subjects listened to the emotional synthetic utterances of “angry,” “happy,” “neutral,” “sad,” or “surprised” when the utterance was the Japanese name “Taro.”

Hierarchical Prosody Conversion Using Regression-Based Clustering for Emotional Speech Synthesis

2010 ◽  
Vol 18 (6) ◽  
pp. 1394-1405 ◽  
Author(s):  
Chung-Hsien Wu ◽  
Chi-Chun Hsia ◽  
Chung-Han Lee ◽  
Mai-Chun Lin
Keyword(s):  
Speech Synthesis ◽  
Emotional Speech
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