Ensemble methods for spoken emotion recognition in call-centres
Speech Communication
Primitives-based evaluation and estimation of emotions in speech
Speech Communication
Fear-type emotion recognition for future audio-based surveillance systems
Speech Communication
Expert Systems with Applications: An International Journal
Emotion recognition from speech signals using new harmony features
Signal Processing
Class-level spectral features for emotion recognition
Speech Communication
Spoken emotion recognition using hierarchical classifiers
Computer Speech and Language
Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection
IEEE Transactions on Audio, Speech, and Language Processing
Editorial: Modeling the cognitive antecedents and consequences of emotion
Cognitive Systems Research
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Automatic emotion recognition from speech signals is one of the important research areas, which adds value to machine intelligence. Pitch, duration, energy and Mel-frequency cepstral coefficients (MFCC) are the widely used features in the field of speech emotion recognition. A single classifier or a combination of classifiers is used to recognize emotions from the input features. The present work investigates the performance of the features of Autoregressive (AR) parameters, which include gain and reflection coefficients, in addition to the traditional linear prediction coefficients (LPC), to recognize emotions from speech signals. The classification performance of the features of AR parameters is studied using discriminant, k-nearest neighbor (KNN), Gaussian mixture model (GMM), back propagation artificial neural network (ANN) and support vector machine (SVM) classifiers and we find that the features of reflection coefficients recognize emotions better than the LPC. To improve the emotion recognition accuracy, we propose a class-specific multiple classifiers scheme, which is designed by multiple parallel classifiers, each of which is optimized to a class. Each classifier for an emotional class is built by a feature identified from a pool of features and a classifier identified from a pool of classifiers that optimize the recognition of the particular emotion. The outputs of the classifiers are combined by a decision level fusion technique. The experimental results show that the proposed scheme improves the emotion recognition accuracy. Further improvement in recognition accuracy is obtained when the scheme is built by including MFCC features in the pool of features.