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Titlebook: Robust Emotion Recognition using Spectral and Prosodic Features; K. Sreenivasa Rao,Shashidhar G. Koolagudi Book 2013 The Author(s) 2013 Sp

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樓主: Malicious
11#
發(fā)表于 2025-3-23 11:05:14 | 只看該作者
12#
發(fā)表于 2025-3-23 13:52:43 | 只看該作者
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發(fā)表于 2025-3-23 19:39:08 | 只看該作者
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發(fā)表于 2025-3-24 02:00:37 | 只看該作者
15#
發(fā)表于 2025-3-24 05:59:49 | 只看該作者
Book 2013d syllable levels in different parts of the utterance) features for discerning emotions in a robust manner.The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. F
16#
發(fā)表于 2025-3-24 08:02:56 | 只看該作者
Robust Emotion Recognition using Speaking Rate Features,cond stage emotions in each broad group are further classified. Spectral and prosodic features are explored in each stage for discriminating the emotions. Combination of spectral and prosodic features is observed to be performed better.
17#
發(fā)表于 2025-3-24 12:38:43 | 只看該作者
Book 2013eatures based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus.
18#
發(fā)表于 2025-3-24 15:30:34 | 只看該作者
Introduction,speech emotion recognition are mentioned. Important state-of-the-art issues prevailing in the area of emotional speech processing are discussed at the end of the chapter along with a note on the organization of the book.
19#
發(fā)表于 2025-3-24 20:29:18 | 只看該作者
Robust Emotion Recognition using Combination of Excitation Source, Spectral and Prosodic Features,ng in nature. By properly exploiting these evidences, the recognition performance will definitely improved. From the results, its is observed that all the combinations explored in this have enhanced the recognition performance significantly.
20#
發(fā)表于 2025-3-25 01:53:24 | 只看該作者
Emotion Recognition on Real Life Emotions,motions. Single and multi-speaker data is collected to study the speaker influence on emotion recognition. Different features are explored for identifying the collected emotions. From the results, it is observed that spectral features carry robust emotion specific information.
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