Usefulness of the LPC-residue in text-independent speaker verification
Speech Communication
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Emotional stress in synthetic speech: progress and future directions
Speech Communication - Special issue on speech under stress
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
AANN: an alternative to GMM for pattern recognition
Neural Networks
Describing the emotional states that are expressed in speech
Speech Communication - Special issue on speech and emotion
A corpus-based speech synthesis system with emotion
Speech Communication - Special issue on speech and emotion
Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Artificial Neural Networks
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Pitch Synchronous Analysis Method and Fisher Criterion Based Speaker Identification
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Feature Combination for Better Differentiating Anger from Neutral in Mandarin Emotional Speech
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Emotion Recognition in Chinese Natural Speech by Combining Prosody and Voice Quality Features
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Intonation modeling for Indian languages
Computer Speech and Language
Adaptive and Optimal Classification of Speech Emotion Recognition
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 05
Statistical Evaluation of Speech Features for Emotion Recognition
ICDT '09 Proceedings of the 2009 Fourth International Conference on Digital Telecommunications
Features extraction for speech emotion
Journal of Computational Methods in Sciences and Engineering
Determining mixing parameters from multispeaker data using speech-specific information
IEEE Transactions on Audio, Speech, and Language Processing
Exploring Speech Features for Classifying Emotions along Valence Dimension
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Study on speech emotion recognition system in E-learning
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Class-level spectral features for emotion recognition
Speech Communication
Spectral mapping using artificial neural networks for voice conversion
IEEE Transactions on Audio, Speech, and Language Processing
Combining acoustic features for improved emotion recognition in mandarin speech
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Vowel Onset Point Detection Using Source, Spectral Peaks, and Modulation Spectrum Energies
IEEE Transactions on Audio, Speech, and Language Processing
Epoch Extraction From Speech Signals
IEEE Transactions on Audio, Speech, and Language Processing
Emotion recognition from speech using global and local prosodic features
International Journal of Speech Technology
Characterization and recognition of emotions from speech using excitation source information
International Journal of Speech Technology
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In this work, source, system, and prosodic features of speech are explored for characterizing and classifying the underlying emotions. Different speech features contribute in different ways to express the emotions, due to their complementary nature. Linear prediction residual samples chosen around glottal closure regions, and glottal pulse parameters are used to represent excitation source information. Linear prediction cepstral coefficients extracted through simple block processing and pitch synchronous analysis represent the vocal tract information. Global and local prosodic features extracted from gross statistics and temporal dynamics of the sequence of duration, pitch, and energy values represent the prosodic information. Emotion recognition models are developed using above mentioned features separately, and in combination. Simulated Telugu emotion database (IITKGP-SESC) is used to evaluate the proposed features. The emotion recognition results obtained using IITKGP-SESC are compared with the results of internationally known Berlin emotion speech database (Emo-DB). Autoassociative neural networks, Gaussian mixture models, and support vector machines are used to develop emotion recognition systems with source, system, and prosodic features, respectively. Weighted combination of evidence has been used while combining the performance of systems developed using different features. From the results, it is observed that, each of the proposed speech features has contributed toward emotion recognition. The combination of features improved the emotion recognition performance, indicating the complementary nature of the features.