scholarly journals Assessment of spontaneous emotional speech database toward emotion recognition: Intensity and similarity of perceived emotion from spontaneously expressed emotional speech

2011 ◽  
Vol 32 (1) ◽  
pp. 26-29 ◽  
Author(s):  
Yoshiko Arimoto ◽  
Sumio Ohno ◽  
Hitoshi Iida
Keyword(s):  
Emotion Recognition ◽  
Emotional Speech ◽  
Speech Database ◽  
Emotional Speech Database ◽  
Perceived Emotion

scholarly journals Emotional Speech Database and the Acoustic Analysis of Emotional Speech

EONEOHAG ◽  
2015 ◽  
Vol null (72) ◽  
pp. 175-199
Author(s):  
손남호 ◽  
Hwang Hyosung ◽  
Ho-Young Lee
Keyword(s):  
Acoustic Analysis ◽  
Emotional Speech ◽  
Speech Database ◽  
Emotional Speech Database

scholarly journals Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features

2019 ◽  
Vol 9 (12) ◽  
pp. 2470 ◽  
Author(s):  
Anvarjon Tursunov ◽  
Soonil Kwon ◽  
Hee-Suk Pang
Keyword(s):  
Short Term Memory ◽  
Classification Performance ◽  
Support Vector ◽  
Emotional Speech ◽  
Acoustic Features ◽  
Discrete Emotions ◽  
Forward Selection ◽  
Mel Frequency Cepstral Coefficients ◽  
Speech Database ◽  
Emotional Speech Database

The most used and well-known acoustic features of a speech signal, the Mel frequency cepstral coefficients (MFCC), cannot characterize emotions in speech sufficiently when a classification is performed to classify both discrete emotions (i.e., anger, happiness, sadness, and neutral) and emotions in valence dimension (positive and negative). The main reason for this is that some of the discrete emotions, such as anger and happiness, share similar acoustic features in the arousal dimension (high and low) but are different in the valence dimension. Timbre is a sound quality that can discriminate between two sounds even with the same pitch and loudness. In this paper, we analyzed timbre acoustic features to improve the classification performance of discrete emotions as well as emotions in the valence dimension. Sequential forward selection (SFS) was used to find the most relevant acoustic features among timbre acoustic features. The experiments were carried out on the Berlin Emotional Speech Database and the Interactive Emotional Dyadic Motion Capture Database. Support vector machine (SVM) and long short-term memory recurrent neural network (LSTM-RNN) were used to classify emotions. The significant classification performance improvements were achieved using a combination of baseline and the most relevant timbre acoustic features, which were found by applying SFS on a classification of emotions for the Berlin Emotional Speech Database. From extensive experiments, it was found that timbre acoustic features could characterize emotions sufficiently in a speech in the valence dimension.

scholarly journals Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

2020 ◽  
Vol 10 (5) ◽  
pp. 4752
Author(s):  
Vishal P. Tank ◽  
S. K. Hadia
Keyword(s):  
Artificial Intelligence ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Emotional States ◽  
Emotional Speech ◽  
Speech Corpus ◽  
Speech Database ◽  
Machine Communication ◽  
Gujarati Language

In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official’s language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. To observe effect of different emotions, analysis of proposed Gujarati speech database is carried out using efficient speech parameters like pitch, energy and MFCC using MATLAB Software.

scholarly journals Mexican Emotional Speech Database Based on Semantic, Frequency, Familiarity, Concreteness, and Cultural Shaping of Affective Prosody

Data ◽  
2021 ◽  
Vol 6 (12) ◽  
pp. 130
Author(s):  
Mathilde Marie Duville ◽  
Luz María Alonso-Valerdi ◽  
David I. Ibarra-Zarate
Keyword(s):  
Statistical Analysis ◽  
Emotional Expression ◽  
Support Vector ◽  
Cultural Variation ◽  
Emotional Prosody ◽  
Emotional Speech ◽  
Linguistic Features ◽  
Male Adult ◽  
Speech Database ◽  
Emotional Speech Database

In this paper, the Mexican Emotional Speech Database (MESD) that contains single-word emotional utterances for anger, disgust, fear, happiness, neutral and sadness with adult (male and female) and child voices is described. To validate the emotional prosody of the uttered words, a cubic Support Vector Machines classifier was trained on the basis of prosodic, spectral and voice quality features for each case study: (1) male adult, (2) female adult and (3) child. In addition, cultural, semantic, and linguistic shaping of emotional expression was assessed by statistical analysis. This study was registered at BioMed Central and is part of the implementation of a published study protocol. Mean emotional classification accuracies yielded 93.3%, 89.4% and 83.3% for male, female and child utterances respectively. Statistical analysis emphasized the shaping of emotional prosodies by semantic and linguistic features. A cultural variation in emotional expression was highlighted by comparing the MESD with the INTERFACE for Castilian Spanish database. The MESD provides reliable content for linguistic emotional prosody shaped by the Mexican cultural environment. In order to facilitate further investigations, a corpus controlled for linguistic features and emotional semantics, as well as one containing words repeated across voices and emotions are provided. The MESD is made freely available.

The Mexican Emotional Speech Database (MESD): elaboration and assessment based on machine learning*

2021 ◽  
Author(s):  
Mathilde M. Duville ◽  
Luz M. Alonso-Valerdi ◽  
David I. Ibarra-Zarate
Keyword(s):  
Machine Learning ◽  
Emotional Speech ◽  
Speech Database ◽  
Emotional Speech Database

Voice and emotional expression transformation based on statistics of vowel parameters in an emotional speech database

2005 ◽  
Author(s):  
Toru Takahashi ◽  
Takeshi Fujii ◽  
Masashi Nishi ◽  
Hideki Banno ◽  
Toshio Irino ◽  
...  
Keyword(s):  
Emotional Expression ◽  
Emotional Speech ◽  
Speech Database ◽  
Emotional Speech Database

scholarly journals Nonlinear Dynamic Feature Extraction Based on Phase Space Reconstruction for the Classification of Speech and Emotion

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Ying Sun ◽  
Xue-Ying Zhang ◽  
Jiang-He Ma ◽  
Chun-Xiao Song ◽  
Hui-Fen Lv
Keyword(s):  
Feature Extraction ◽  
Phase Space ◽  
Speech Signal ◽  
Nonlinear Dynamic ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Speech Signals ◽  
Emotional Speech ◽  
Speech Database ◽  
Emotional Speech Database

Due to the shortcomings of linear feature parameters in speech signals, and the limitations of existing time- and frequency-domain attribute features in characterizing the integrity of the speech information, in this paper, we propose a nonlinear method for feature extraction based on the phase space reconstruction (PSR) theory. First, the speech signal was analyzed using a nonlinear dynamic model. Then, the model was used to reconstruct a one-dimensional time speech signal. Finally, nonlinear dynamic (NLD) features based on the reconstruction of the phase space were extracted as the new characteristic parameters. Then, the performance of NLD features was verified by comparing their recognition rates with those of other features (NLD features, prosodic features, and MFCC features). Finally, the Korean isolated words database, the Berlin emotional speech database, and the CASIA emotional speech database were chosen for validation. The effectiveness of the NLD features was tested using the Support Vector Machine classifier. The results show that NLD features not only have high recognition rate and excellent antinoise performance for speech recognition tasks but also can fully characterize the different emotions contained in speech signals.

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