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

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.

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Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features

Applied Sciences ◽

10.3390/app9122470 ◽

2019 ◽

Vol 9 (12) ◽

pp. 2470 ◽

Cited By ~ 7

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.

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

10.21437/interspeech.2005-585 ◽

2005 ◽

Author(s):

Toru Takahashi ◽

Takeshi Fujii ◽

Masashi Nishi ◽

Hideki Banno ◽

Toshio Irino ◽

...

Keyword(s):

Emotional Expression ◽

Emotional Speech ◽

Speech Database ◽

Emotional Speech Database

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

Mathematical Problems in Engineering ◽

10.1155/2020/9452976 ◽

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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Kannada Emotional Speech Database: Design, Development and Evaluation

Proceedings of International Conference on Cognition and Recognition - Lecture Notes in Networks and Systems ◽

10.1007/978-981-10-5146-3_14 ◽

2017 ◽

pp. 135-143

Author(s):

A. Geethashree ◽

D J Ravi

Keyword(s):

Database Design ◽

Emotional Speech ◽

Design Development ◽

Speech Database ◽

Emotional Speech Database

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

Acoustical Science and Technology ◽

10.1250/ast.32.26 ◽

2011 ◽

Vol 32 (1) ◽

pp. 26-29 ◽

Cited By ~ 2

Author(s):

Yoshiko Arimoto ◽

Sumio Ohno ◽

Hitoshi Iida

Keyword(s):

Emotion Recognition ◽

Emotional Speech ◽

Speech Database ◽

Emotional Speech Database ◽

Perceived Emotion

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A combined cepstral distance method for emotional speech recognition

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417719836 ◽

2017 ◽

Vol 14 (4) ◽

pp. 172988141771983 ◽

Cited By ~ 2

Author(s):

Changqin Quan ◽

Bin Zhang ◽

Xiao Sun ◽

Fuji Ren

Keyword(s):

Artificial Intelligence ◽

Speech Recognition ◽

Affective Computing ◽

Recognition Rate ◽

Support Vector ◽

Emotional Speech ◽

Emotion Classification ◽

Distance Method ◽

Speech Database ◽

Emotional Speech Recognition

Affective computing is not only the direction of reform in artificial intelligence but also exemplification of the advanced intelligent machines. Emotion is the biggest difference between human and machine. If the machine behaves with emotion, then the machine will be accepted by more people. Voice is the most natural and can be easily understood and accepted manner in daily communication. The recognition of emotional voice is an important field of artificial intelligence. However, in recognition of emotions, there often exists the phenomenon that two emotions are particularly vulnerable to confusion. This article presents a combined cepstral distance method in two-group multi-class emotion classification for emotional speech recognition. Cepstral distance combined with speech energy is well used as speech signal endpoint detection in speech recognition. In this work, the use of cepstral distance aims to measure the similarity between frames in emotional signals and in neutral signals. These features are input for directed acyclic graph support vector machine classification. Finally, a two-group classification strategy is adopted to solve confusion in multi-emotion recognition. In the experiments, Chinese mandarin emotion database is used and a large training set (1134 + 378 utterances) ensures a powerful modelling capability for predicting emotion. The experimental results show that cepstral distance increases the recognition rate of emotion sad and can balance the recognition results with eliminating the over fitting. And for the German corpus Berlin emotional speech database, the recognition rate between sad and boring, which are very difficult to distinguish, is up to 95.45%.

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