scholarly journals Evaluation of influence of spectral and prosodic features on GMM classification of Czech and Slovak emotional speech

Author(s):  
Jiří Přibil ◽  
Anna Přibilová
2013 ◽  
Vol 1 (1) ◽  
pp. 54-67
Author(s):  
Kanu Boku ◽  
Taro Asada ◽  
Yasunari Yoshitomi ◽  
Masayoshi Tabuse

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.”


Author(s):  
Gustavo Assunção ◽  
Paulo Menezes ◽  
Fernando Perdigão

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>The idea of recognizing human emotion through speech (SER) has recently received considerable attention from the research community, mostly due to the current machine learning trend. Nevertheless, even the most successful methods are still rather lacking in terms of adaptation to specific speakers and scenarios, evidently reducing their performance when compared to humans. In this paper, we evaluate a largescale machine learning model for classification of emotional states. This model has been trained for speaker iden- tification but is instead used here as a front-end for extracting robust features from emotional speech. We aim to verify that SER improves when some speak- er</span><span>’</span><span>s emotional prosody cues are considered. Experiments using various state-of- the-art classifiers are carried out, using the Weka software, so as to evaluate the robustness of the extracted features. Considerable improvement is observed when comparing our results with other SER state-of-the-art techniques.</span></p></div></div></div>


2017 ◽  
Vol 43 (8) ◽  
pp. 4289-4302 ◽  
Author(s):  
Nagaratna B. Chittaragi ◽  
Ambareesh Prakash ◽  
Shashidhar G. Koolagudi

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