Multistage classification scheme to enhance speech emotion recognition

Poorna, S. S.; Nair, G. J.

doi:10.1007/s10772-019-09605-w

Cited by 28 publications

(4 citation statements)

References 39 publications

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“…It extracts, identifies, and classifies valuable emotional features, and builds models with corresponding parameters. The updated parameters can then be trained and matched by computer means to obtain the corresponding recognition results ( Poorna and Nair, 2019 ). Different from speech recognition, speech emotion recognition utilizes the personality characteristics of different emotional states.…”

Section: English Flipped Classroom Teaching Mode Methods Based On Emo...mentioning

confidence: 99%

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Lin

2022

Front. Psychol.

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With the development of modern information technology, the flipped classroom teaching mode came into being. It has gradually become one of the hotspots of contemporary educational circles and has been applied to various disciplines at the same time. The domestic research on the flipped classroom teaching mode is still in the exploratory stage. The application of flipped classroom teaching mode is still in the exploratory stage. It also has many problems, such as low class efficiency, poor teacher-student interaction, outdated teaching modes, not student-centered, etc., which lead to poor students’ enthusiasm for learning. Therefore, the current English flipped classroom teaching mode still needs to be tested and revised in practice. Combined with emotion recognition technology, this paper analyzes speech emotion recognition, image emotion recognition, and audition emotion recognition technology and conducts a revision test for the current English flipped classroom teaching mode. It uses the SVM algorithm for one-to-one method and dimension discretization for emotion recognition, and finds that the recognition results after different dimension classification recognition are improved for each emotion. Among them, the recognition rate of different dimension classification recognition methods is 2.6% higher than that of one-to-one method. This shows that under the same conditions, the emotion recognition technology of different dimension classification recognition methods is higher.

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Section: English Flipped Classroom Teaching Mode Methods Based On Emo...mentioning

confidence: 99%

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Lin

2022

Front. Psychol.

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“…Poorna et al [18] developed a speech emotion recognition mechanism for the Arabic population. A speech database elicited emotions such as surprise, anger, disgust, happiness, neutrality, and sadness was developed from 14 non-native yet efficient speakers of the language.…”

Section: Related Workmentioning

confidence: 99%

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Alwayle¹,

Al-onazi²,

Alalayah³

et al. 2023

Computer Systems Science and Engineering

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Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

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“…According to [16,17], the techniques in neu-ral style transfer [18] can be applied for spectrograms as it is the two-dimensional representations of audio frequencies with respect to time. In [19], Poorna et al applied a multistage learning network for classifying speech emotions in Arabic speaking community. Kown et al [20] proposed an artificial intelligence-assisted deep stride convolution neural network architecture using the plain nets strategy to learn discriminative and salient features from spectrogram of speech signals that are enhanced in prior steps to perform better.…”

Section: Related Workmentioning

confidence: 99%

Elder emotion classification through multimodal fusion of intermediate layers and cross-modal transfer learning

Sreevidya

Veni

Murthy

2022

SIViP

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The objective of the work is to develop an automated emotion recognition system specifically targeted to elderly people. A multi-modal system is developed which has integrated information from audio and video modalities. The database selected for experiments is ElderReact, which contains 1323 video clips of 3 to 8 s duration of people above the age of 50. Here, all the six available emotions Disgust, Anger, Fear, Happiness, Sadness and Surprise are considered. In order to develop an automated emotion recognition system for aged adults, we attempted different modeling techniques. Features are extracted, and neural network models with backpropagation are attempted for developing the models. Further, for the raw video model, transfer learning from pretrained networks is attempted. Convolutional neural network and long short-time memory-based models were taken by maintaining the continuity in time between the frames while capturing the emotions. For the audio model, cross-model transfer learning is applied. Both the models are combined by fusion of intermediate layers. The layers are selected through a grid-based search algorithm. The accuracy and F1-score show that the proposed approach is outperforming the state-of-the-art results. Classification of all the images shows a minimum relative improvement of 6.5% for happiness to a maximum of 46% increase for sadness over the baseline results.

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Multistage classification scheme to enhance speech emotion recognition

Cited by 28 publications

References 39 publications

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Elder emotion classification through multimodal fusion of intermediate layers and cross-modal transfer learning

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