14,256 research outputs found
Proposing a hybrid approach for emotion classification using audio and video data
Emotion recognition has been a research topic in the field of Human-Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have
become interested in emotion recognition and classification using different sources. A hybrid
approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this
approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification
accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%
Employing Emotion Cues to Verify Speakers in Emotional Talking Environments
Usually, people talk neutrally in environments where there are no abnormal
talking conditions such as stress and emotion. Other emotional conditions that
might affect people talking tone like happiness, anger, and sadness. Such
emotions are directly affected by the patient health status. In neutral talking
environments, speakers can be easily verified, however, in emotional talking
environments, speakers cannot be easily verified as in neutral talking ones.
Consequently, speaker verification systems do not perform well in emotional
talking environments as they do in neutral talking environments. In this work,
a two-stage approach has been employed and evaluated to improve speaker
verification performance in emotional talking environments. This approach
employs speaker emotion cues (text-independent and emotion-dependent speaker
verification problem) based on both Hidden Markov Models (HMMs) and
Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. The approach is
comprised of two cascaded stages that combines and integrates emotion
recognizer and speaker recognizer into one recognizer. The architecture has
been tested on two different and separate emotional speech databases: our
collected database and Emotional Prosody Speech and Transcripts database. The
results of this work show that the proposed approach gives promising results
with a significant improvement over previous studies and other approaches such
as emotion-independent speaker verification approach and emotion-dependent
speaker verification approach based completely on HMMs.Comment: Journal of Intelligent Systems, Special Issue on Intelligent
Healthcare Systems, De Gruyter, 201
Determination of Formant Features in Czech and Slovak for GMM Emotional Speech Classifier
The paper is aimed at determination of formant features (FF) which describe vocal tract characteristics. It comprises analysis of the first three formant positions together with their bandwidths and the formant tilts. Subsequently, the statistical evaluation and comparison of the FF was performed. This experiment was realized with the speech material in the form of sentences of male and female speakers expressing four emotional states (joy, sadness, anger, and a neutral state) in Czech and Slovak languages. The statistical distribution of the analyzed formant frequencies and formant tilts shows good differentiation between neutral and emotional styles for both voices. Contrary to it, the values of the formant 3-dB bandwidths have no correlation with the type of the speaking style or the type of the voice. These spectral parameters together with the values of the other speech characteristics were used in the feature vector for Gaussian mixture models (GMM) emotional speech style classifier that is currently developed. The overall mean classification error rate achieves about 18 %, and the best obtained error rate is 5 % for the sadness style of the female voice. These values are acceptable in this first stage of development of the GMM classifier that should be used for evaluation of the synthetic speech quality after applied voice conversion and emotional speech style transformation
Feature extraction based on bio-inspired model for robust emotion recognition
Emotional state identification is an important issue to achieve more natural speech interactive systems. Ideally, these systems should also be able to work in real environments in which generally exist some kind of noise. Several bio-inspired representations have been applied to artificial systems for speech processing under noise conditions. In this work, an auditory signal representation is used to obtain a novel bio-inspired set of features for emotional speech signals. These characteristics, together with other spectral and prosodic features, are used for emotion recognition under noise conditions. Neural models were trained as classifiers and results were compared to the well-known mel-frequency cepstral coefficients. Results show that using the proposed representations, it is possible to significantly improve the robustness of an emotion recognition system. The results were also validated in a speaker independent scheme and with two emotional speech corpora.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin
- …