Speech emotion recognition based on a modified brain emotional learning model

Motamed, Sara; Setayeshi, Saeed; Rabiee, Azam

doi:10.1016/j.bica.2016.12.002

Cited by 41 publications

(16 citation statements)

References 15 publications

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“…A positive emotion of a user of AI could then serve as a positive reward, and vice versa for a user's negative emotion for an AI relying on reinforcement learning. First attempts in this direction exist (Motamed, Setayeshi, & Rabiee, 2017), but more is expected to come.…”

Section: Reinforced Learningmentioning

confidence: 99%

A Review on Five Recent and Near-Future Developments in Computational Processing of Emotion in the Human Voice

Schuller¹,

Schuller

2020

Emotion Review

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We provide a short review on the recent and near-future developments of computational processing of emotion in the voice, highlighting (a) self-learning of representations moving continuously away from traditional expert-crafted or brute-forced feature representations to end-to-end learning, (b) a movement towards the coupling of analysis and synthesis of emotional voices to foster better mutual understanding, (c) weakly supervised learning at a large scale, (d) transfer learning from related domains such as speech recognition or cross-modal transfer learning, and (e) reinforced learning through interactive applications at a large scale. For each of these trends, we shortly explain their implications and potential use such as for interpretation in psychological studies and usage in digital health and digital psychology applications. We also discuss further potential development.

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Section: Reinforced Learningmentioning

confidence: 99%

A Review on Five Recent and Near-Future Developments in Computational Processing of Emotion in the Human Voice

Schuller¹,

Schuller

2020

Emotion Review

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“…A 60.89% test set unweighted average recall (UAR) was achieved for frame-based emotion recognition using the CNN system. Motamed et al (2017) introduced an optimized brain emotional learning model (BEL) that merged an adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) model for speech emotion recognition. The ANFIS was intended to model the human amygdala and orbitofrontal cortex in order to make rules that were passed to the MLP network.…”

Section: Emotion Recognition Using Speechmentioning

confidence: 99%

“…The review of speech emotion recognition showed that there are numerous corpora available for model research, validation, and testing (Haq et al, 2008;Grimm et al, 2008;Canavan et al, 1997;Durston et al, 2001;Engbert and Hansen, 2007;Burkhardt et al, 2005;Hansen and Bou-Gazale, 1997;Martin et al, 2006;Schuller et al, 2007;Steininger et al, 2002). Speech features used for input to emotion recognition models include MFCC (Motamed et al, 2017;Wu et al, 2011), prosody (Dai et al, 2015;Wu et al, 2011;Fernandez and Picard, 2011), and voice-quality (Fernandez and Picard, 2011;Dai et al, 2015) features. The use of a pre-compiled, publically available speech feature set was investigated as part of the work undertaken by C. K.…”

Section: Emotion Recognition Using Speechmentioning

confidence: 99%

Affective computing using speech and eye gaze: a review and bimodal system proposal for continuous affect prediction

O'Dwyer,

Murray,

Flynn

2018

Preprint

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Speech has been a widely used modality in the field of affective computing. Recently however, there has been a growing interest in the use of multi-modal affective computing systems. These multi-modal systems incorporate both verbal and non-verbal features for affective computing tasks. Such multi-modal affective computing systems are advantageous for emotion assessment of individuals in audio-video communication environments such as teleconferencing, healthcare, and education. From a review of the literature, the use of eye gaze features extracted from video is a modality that has remained largely unexploited for continuous affect prediction. This work presents a review of the literature within the emotion classification and continuous affect prediction sub-fields of affective computing for both speech and eye gaze modalities. Additionally, continuous affect prediction experiments using speech and eye gaze modalities are presented. A baseline system is proposed using open source software, the performance of which is assessed on a publicly available audio-visual corpus. Further system performance is assessed in a cross-corpus and cross-lingual experiment.The experimental results suggest that eye gaze is an effective supportive modal-$ Paper submission for review

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“…Firstly, there is an uncertainty in the definition of speech emotion model. There are two main types of speech emotion model definition [3]. One is the speech emotion model of the discrete categories; the other is the speech emotion model of the continuous dimensions.…”

Section: Introductionmentioning

confidence: 99%

A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

Wei

Zhao

2019

Pers Ubiquit Comput

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Since the contextual information has an important impact on the speaker's emotional state, how to use emotion-related context information to conduct feature learning is a key problem. The existing speech emotion recognition algorithms achieve the relatively high recognition rate; these algorithms are not very good application to the real-life speech emotion recognition systems. Therefore, in order to address the abovementioned issues, a novel speech emotion recognition algorithm based on improved stacked kernel sparse deep model is proposed in this paper, which is based on auto-encoder, denoising auto-encoder, and sparse auto-encoder to improve the Chinese speech emotion recognition. The first layer of the structure uses a denoising autoencoder to learn a hidden feature with a larger dimension than the dimension of the input features, and the second layer employs a sparse auto-encoder to learn sparse features. Finally, a wavelet-kernel sparse SVM classifier is applied to classify the features. The proposed algorithm is evaluated on the testing dataset, which contains the speech emotion data of spontaneous, non-prototypical, and long-term. The experimental results show that the proposed algorithm outperforms the existing state-of-the-art algorithms in speech emotion recognition.

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Speech emotion recognition based on a modified brain emotional learning model

Cited by 41 publications

References 15 publications

A Review on Five Recent and Near-Future Developments in Computational Processing of Emotion in the Human Voice

A Review on Five Recent and Near-Future Developments in Computational Processing of Emotion in the Human Voice

Affective computing using speech and eye gaze: a review and bimodal system proposal for continuous affect prediction

A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

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