Automatic voice emotion recognition of child-parent conversations in natural settings

Law, Effie Lai‐Chong; Soleimani, Samaneh; Watkins, Dawn; Barwick, Joanna

doi:10.1080/0144929x.2020.1741684

Cited by 6 publications

(3 citation statements)

References 84 publications

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“…Accordingly, emotional changes can be judged. Therefore, studies have been actively conducted to find the emotional state of a person and recognize his/her emotions through a pattern analysis of the vibration, pitch, or other types of voice information [30]. Law et al [30] proposed an automatic voice emotion recognition method in parent-child conversations.…”

Section: Voice-recognition-based Classification Of Emotionsmentioning

confidence: 99%

“…Therefore, studies have been actively conducted to find the emotional state of a person and recognize his/her emotions through a pattern analysis of the vibration, pitch, or other types of voice information [30]. Law et al [30] proposed an automatic voice emotion recognition method in parent-child conversations. The proposed method uses a support vector machine to improve the accuracy of automatic speech recognition to analyze a conversation between a parent and a child in their everyday life.…”

Section: Voice-recognition-based Classification Of Emotionsmentioning

confidence: 99%

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CNN-Based Voice Emotion Classification Model for Risk Detection

Yoo¹,

Baek²,

Chung³

2021

Intelligent Automation &Amp; Soft Computing

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With the convergence and development of the Internet of things (IoT) and artificial intelligence, closed-circuit television, wearable devices, and artificial neural networks have been combined and applied to crime prevention and followup measures against crimes. However, these IoT devices have various limitations based on the physical environment and face the fundamental problem of privacy violations. In this study, voice data are collected and emotions are classified based on an acoustic sensor that is free of privacy violations and is not sensitive to changes in external environments, to overcome these limitations. For the classification of emotions in the voice, the data generated from an acoustic sensor are combined with the convolution neural network algorithm of an artificial neural network. Short-time Fourier transform and wavelet transform as frequency spectrum representation methods are used as preprocessing techniques for the analysis of a pattern of acoustic data. The preprocessed spectrum data are represented as a 2D image of the pattern of emotion felt through hearing, which is applied to the image classification learning model of an artificial neural network. The image classification learning model uses the ResNet. The artificial neural network internally uses various forms of gradient descent to compare the learning of each node and analyzes the pattern through a feature map. The classification model facilitates the classification of voice data into three emotion types: angry, fearful, and surprised. Thus, a system that can detect situations around sensors and predict danger can be established. Despite the different emotional intensities of the base data and sentence-based learning data, the established voice classification model demonstrated an accuracy of more than 77.2%. This model is applicable to various areas, including the prediction of crime situations and the management of work environments for emotional labor.

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Section: Voice-recognition-based Classification Of Emotionsmentioning

confidence: 99%

Section: Voice-recognition-based Classification Of Emotionsmentioning

confidence: 99%

CNN-Based Voice Emotion Classification Model for Risk Detection

Yoo¹,

Baek²,

Chung³

2021

Intelligent Automation &Amp; Soft Computing

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“…Automatic emotion recognition provides an opportunity to understand how emotion patterns in daily life are associated with health, both mental and physical [1,2]. The inexpensive production of audio recording-capable devices has made speech emotion recognition (SER) an attractive avenue for the deployment of emotion recognition systems and recent advances in machine learning have led to improved accuracy in state-of-the-art SER systems, but robustness to additive noise in SER is still an open problem.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training

Wilf¹,

Provost²

2020

Preprint

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Robustness to environmental noise is important to creating automatic speech emotion recognition systems that are deployable in the real world. Prior work on noise robustness has assumed that systems would not make use of sampleby-sample training noise conditions, or that they would have access to unlabelled testing data to generalize across noise conditions. We avoid these assumptions and introduce the resulting task as heterogeneous condition training. We show that with full knowledge of the test noise conditions, we can improve performance by dynamically routing samples to specialized feature encoders for each noise condition, and with partial knowledge, we can use known noise conditions and domain adaptation algorithms to train systems that generalize well to unseen noise conditions. We then extend these improvements to the multimodal setting by dynamically routing samples to maintain temporal ordering, resulting in significant improvements over approaches that do not specialize or generalize based on noise type.

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