Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture

By Björn Schuller, Gerhard Rigoll, Manfred K. Lang. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004

484 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/af67a379870239dc7630181a1583dbddc49ce439

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Citations in the corpus

Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching (2018, methodology): “Model (GMM) [27], Hidden Markov Models (HMM) [28], and SVM [29], are widely adopted for speech emotion recognition.”
Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks (2014, methodology): “In [34], a spotting algorithm that searches for emotional keywords or phrases in the utterances was employed.”
Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, background): “Linear classifiers usually perform classification based on object features with a linear arrangement of various objects [40]–[43].”
Paralinguistics in speech and language - State-of-the-art and the challenge (2013, background): “, 2011f), and more systematic classifier and regressor optimisation and tailored architectures (hierarchical (Yoon and Park, 2011) and hybrid (Schuller et al., 2004) or ensembles (Schuller et al.”
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011, methodology): “…Boucouvalas, 2002), Latent Semantic Analysis (Goertzel et al., 2000), Transformation-based Learning (Wu et al., 2005), World-knowledge-Modelling (Liu et al., 2003), Key-Phrase-Spotting (Schuller et al., 2004), String Kernels (Schuller et al., 2009a), and Bayesian Networks (Breese and Ball, 1998).”
Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network (2017, background): “It can also be used for in-car board system where information of the mental condition of the driver may be provided to the system to initiate safety procedures, if required [4].”
Speech Emotion Recognition Using Fourier Parameters (2015, methodology): “It was also used for speech emotion recognition [4], [28], [29], [50] and outperformed other well-known classifiers [1].”
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011 (2012, methodology): “Similarly in Schuller et al. (2004), linear classifiers , Gaussian Mixture Models, Neural Nets and Support Vector Machines were combined for acoustic feature classification, while a Belief Network spotted emotional key-phrases from an automatic speech recognition (ASR) engine based on Hidden Markov…”
Survey on speech emotion recognition: Features, classification schemes, and databases (2011, methodology): “In [116], the LDA technique is used to compare more than 200 speech features.”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “In light of popular manners of modality combination, we categorize the multi-physical modalities fusion for affective analysis into visual-audio emotion recognition [354,31], text-audio emotion recognition [355,356], and visual-audio-text emotion recognition [357,358].”
Emotional speech recognition: Resources, features, and methods (2006, methodology): “…classifiers may achieve a correct classification rate of 50.5% for four emotional states using energy contours in several frequency bands (Fernandez and Picard, 2003) or 75% for seven emotional states using pitch and energy contour statistics of another data collection (Schüller et al., 2004).”
Emotion recognition from speech: a review (2012, background): “It is particularly useful for enhancing naturalness in speech based human machine interaction (Schuller et al. 2004; Dellert et al. 1996; Koolagudi et al. 2009).”