Survey on speech emotion recognition: Features, classification schemes, and databases

By Moataz M. H. El Ayadi, Mohamed S. Kamel, F. Karray. 2011

2,137 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Experimental and Cognitive Psychology

In Signal Processing

Citations in the corpus

Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? (2019, background): “…speech processing algorithms can infer not only the spoken contents from the speech signal, but also the speaker’s identity [5], intention [6–9], gender [10,11], emotional state [12–14], pathological condition [15–17], personality [18,19] and cultural [20,21] attributes to a great extent.”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “See surveys by El Ayadi, Kamel, and Karray (2011) and Anagnostopoulos, Iliou, and Giannoukos (2015) for an overview of emotion detection in speech.”
Databases, features and classifiers for speech emotion recognition: a review (2018, methodology): “…of the driver’s mental state is conveyed to the operating system of car, (4) diagnostic tool for a therapist to treat disease (5) used as a tool for automatic translation system in which a speaker plays a key role in communication between parties and (6) mobile communications (Ayadi et al. 2011).”
MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception (2017, background): “Data of this type have played a key role in studies addressing how emotionmodulates facial expressions and speech at the phoneme level [1], [2].”
Speech emotion recognition using deep neural network and extreme learning machine (2014, background): “Speech emotion recognition is a very challenging task of which extracting effective emotional features is an open question [1, 2].”
Speech emotion recognition (2018, neutral): “The automatic analysis of emotional speech often adds or even focuses entirely on spectral features, such as formants or selected bandenergies, center of gravity, or roll-off points and cepstral features such as MFCC or mel-frequency bands as well as linear prediction coefficients.(2,15,38) Based on”
A Survey on Deep Learning (2018, background): “While some previous survey papers only focused on a certain scope in deep learning [36, 70], the novelty of this article is that it focuses on different aspects of deep learning by presenting a review of the top-level papers, the authors’ experience, and the breakthroughs in research on and…”
Introducing shared-hidden-layer autoencoders for transfer learning and their application in acoustic emotion recognition (2014, background): “Index Terms— Transfer Learning, Cross-Corpus, SharedHidden-Layer Autoencoder, Emotion Recognition”
Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching (2018, background): “emotional speech datasets [3]–[5] contain limited numbers of samples.”