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DNN-HMM-Based Speaker-Adaptive Emotion Recognition Using MFCC and Epoch-Based Features
12 references·3 citing·39 second hop
DNN-HMM-Based Speaker-Adaptive Emotion Recognition Using MFCC and Epoch-Based Features (2020)DNN-HMM-Based Speaker-Adaptive Emotion Re…Automatic speech emotion recognition using recurrent neural networks with local attention (2017)Automatic speech emotion reco…Speech emotion recognition using deep neural network and extreme learning machine (2014)Speech emotion recognition us…Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies (2010)Cross-Corpus Acoustic Emotion…Multimodal information fusion application to human emotion recognition from face and speech (2009)Multimodal information fusion…IEMOCAP: interactive emotional dyadic motion capture database (2008)IEMOCAP: interactive emotiona…Fear-type emotion recognition for future audio-based surveillance systems (2008)Fear-type emotion recognition…Toward detecting emotions in spoken dialogs (2005)Toward detecting emotions in …A database of German emotional speech (2005)A database of German emotiona…Automatic emotional speech classification (2004)Automatic emotional speech cl…Hidden Markov model-based speech emotion recognition (2003)Hidden Markov model-based spe…Emotion Recognition in Speech Using Neural Networks (2000)Emotion Recognition in Speech…Recognizing emotion in speech (1996)Recognizing emotion in speechAn enhanced speech emotion recognition using vision transformer (2024)An enhanced speech emotion re…Depression Detection in Speech Using Transformer and Parallel Convolutional Neural Networks (2023)Depression Detection in Speec…Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks (2022)Human-Computer Interaction wi…Acoustic profiles in vocal emotion expression. (1996)The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (2015)The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English (2018)Assessing agreement on classification tasks: the kappa statistic (1996)Emotion recognition in human-computer interaction (2001)Analysis of emotion recognition using facial expressions, speech and multimodal information (2004)Emotional speech recognition: Resources, features, and methods (2006)Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011)Toward the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion (1993)Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech (2007)Class-level spectral features for emotion recognition (2010)Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models (2021)Affective Computing (1997)A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions (2008)Deep neural networks for acoustic emotion recognition: Raising the benchmarks (2011)Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition (2013)Emotion Recognition in Speech using Cross-Modal Transfer in the Wild (2018)Coefficient Alpha and the Internal Structure of Tests (1951)The Hidden Dimension (1973)A review of affective computing: From unimodal analysis to multimodal fusion (2017)Speech emotion recognition using deep 1D & 2D CNN LSTM networks (2018)MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (2019)The expression of the emotions in man and animals. (1872)The Measurement of Observer Agreement for Categorical Data (1977)A Survey on Perception Methods for Human–Robot Interaction in Social Robots (2013)Audiovisual emotion recognition in wild (2018)A survey of speech emotion recognition in natural environment (2020)Survey on speech emotion recognition: Features, classification schemes, and databases (2010)End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017)Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers (2019)Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network (2016)Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019)A systematic review on affective computing: emotion models, databases, and recent advances (2022)Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture (2004)EmotionSense (2010)Objective measures, sensors and computational techniques for stress recognition and classification: A survey (2012)Gesture-Based Affective Computing on Motion Capture Data (2005)Recognizing Human Emotional State From Audiovisual Signals (2008)Speech Emotion Recognition Using Support Vector Machine (2012)
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References

  • 2008IEMOCAP: interactive emotional dyadic motion capture database3,864 cited
  • 2005A database of German emotional speech2,365 cited
  • 2005Toward detecting emotions in spoken dialogs969 cited
  • 2014Speech emotion recognition using deep neural network and extreme learning machine822 cited
  • 2017Automatic speech emotion recognition using recurrent neural networks with local attention769 cited
  • 2003Hidden Markov model-based speech emotion recognition594 cited
  • 2010Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies389 cited
  • 2000Emotion Recognition in Speech Using Neural Networks345 cited
  • 2004Automatic emotional speech classification221 cited
  • 2008Fear-type emotion recognition for future audio-based surveillance systems180 cited
  • 2009Multimodal information fusion application to human emotion recognition from face and speech166 cited
  • 1996Recognizing emotion in speech158 cited

Cited by

  • 2024An enhanced speech emotion recognition using vision transformer84 cited
  • 2022Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks83 cited
  • 2023Depression Detection in Speech Using Transformer and Parallel Convolutional Neural Networks

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75 cited