A Comparative Study of Different Weighting Schemes on KNN-Based Emotion Recognition in Mandarin Speech

Pao, Tsang-Long; Chen, Yu-Te; Yeh, Jun-Heng; Cheng, Yun-Maw; Lin, Yu-Yuan

doi:10.1007/978-3-540-74171-8_101

Cited by 26 publications

(13 citation statements)

References 11 publications

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“…Various types of classifiers have been used for the task of SER, including hidden Markov model [26], Gaussian mixture model [38], support vector machine (SVM) [23], artificial neural networks [4], k-nearest neighbor [28] and many others [29]. Among these methods, SVM and HMM are widely used in almost all speech-related applications [25], [26], [9], [40].…”

Section: A Classifiersmentioning

confidence: 99%

Learning Salient Features for Speech Emotion <newline/>Recognition Using Convolutional <newline/>Neural Networks

Mao

Dong

Huang

et al. 2014

IEEE Trans. Multimedia

530

198

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As an essential way of human emotional behavior understanding, speech emotion recognition (SER) has attracted a great deal of attention in human-centered signal processing. Accuracy in SER heavily depends on finding good affect-related, discriminative features. In this paper, we propose to learn affect-salient features for SER using convolutional neural networks (CNN). The training of CNN involves two stages. In the first stage, unlabeled samples are used to learn local invariant features (LIF) using a variant of sparse auto-encoder (SAE) with reconstruction penalization. In the second step, LIF is used as the input to a feature extractor, salient discriminative feature analysis (SDFA), to learn affect-salient, discriminative features using a novel objective function that encourages feature saliency, orthogonality, and discrimination for SER. Our experimental results on benchmark datasets show that our approach leads to stable and robust recognition performance in complex scenes (e.g., with speaker and language variation, and environment distortion) and outperforms several well-established SER features.Index Terms-Affective-salient discriminative feature analysis, convolutional neural networks, feature learning, speech emotion recognition.

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Section: A Classifiersmentioning

confidence: 99%

Learning Salient Features for Speech Emotion <newline/>Recognition Using Convolutional <newline/>Neural Networks

Mao

Dong

Huang

et al. 2014

IEEE Trans. Multimedia

530

198

View full text Add to dashboard Cite

show abstract

“…Related literature [16] uses the Berlin and Hindi emotion databases to classify and train the recognition model and the KNN algorithm to classify different voice messages to obtain emotion classification results. Related literature [17] classification of emotional states of speech spectral features of Mandarin through the KNN algorithm. The related literature [18] uses the nearest neighbour algorithm and local binary mode for image feature extraction and classification to obtain the facial feature emotional state.…”

Section: ) Knn Classificationmentioning

confidence: 99%

Spatial Temporal Variation Graph Convolutional Networks (STV-GCN) for Skeleton-Based Emotional Action Recognition

Tsai

Chen

2021

IEEE Access

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The main core purpose of artificial emotional intelligence is to recognize human emotions. Technologies such as facial, semantic, or brainwave recognition applications have been widely proposed. However, the abovementioned recognition techniques for emotional features require a large number of training samples to obtain high accuracy. Human behaviour pattern can be trained and recognized by the continuous movement of the Spatial Temporal Graph Convolution Network (ST-GCN). However, this technology does not distinguish between the speed of delicate emotions, and the speed of human behaviour and delicate changes of emotions cannot be effectively distinguished. This research paper proposes Spatial Temporal Variation Convolutional Network training for human emotion recognition, using skeleton detection technology to calculate the degree of skeleton point change between consecutive actions and using the nearest neighbour algorithm to classify speed levels and train the ST-GCN recognition model to obtain the emotional state. Application of the speed change recognition ability of the Spatial Temporal Variation Graph Convolution Network (STV-GCN) to artificial emotional intelligence calculation makes it possible to efficiently recognize the delicate actions of happy, sad, fear, and angry in human behaviour. The STV-GCN technology proposed in this paper is compared with ST-GCN and can effectively improve the recognition accuracy by more than 50%. INDEX TERMS Artificial emotional intelligence, spatial temporal graph convolution network, human skeleton joint point.

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“…There are several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs [ 9 ], RFs [ 10 ], the KNN algorithm [ 11 ], hidden Markov models (HMMs) [ 12 ], MLPs [ 13 ], and Gaussian mixture models (GMMs) [ 14 ]. These classifiers have been widely used for speech-related tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network

Farooq

Hussain

Baloch

et al. 2020

Sensors

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Speech emotion recognition (SER) plays a significant role in human–machine interaction. Emotion recognition from speech and its precise classification is a challenging task because a machine is unable to understand its context. For an accurate emotion classification, emotionally relevant features must be extracted from the speech data. Traditionally, handcrafted features were used for emotional classification from speech signals; however, they are not efficient enough to accurately depict the emotional states of the speaker. In this study, the benefits of a deep convolutional neural network (DCNN) for SER are explored. For this purpose, a pretrained network is used to extract features from state-of-the-art speech emotional datasets. Subsequently, a correlation-based feature selection technique is applied to the extracted features to select the most appropriate and discriminative features for SER. For the classification of emotions, we utilize support vector machines, random forests, the k-nearest neighbors algorithm, and neural network classifiers. Experiments are performed for speaker-dependent and speaker-independent SER using four publicly available datasets: the Berlin Dataset of Emotional Speech (Emo-DB), Surrey Audio Visual Expressed Emotion (SAVEE), Interactive Emotional Dyadic Motion Capture (IEMOCAP), and the Ryerson Audio Visual Dataset of Emotional Speech and Song (RAVDESS). Our proposed method achieves an accuracy of 95.10% for Emo-DB, 82.10% for SAVEE, 83.80% for IEMOCAP, and 81.30% for RAVDESS, for speaker-dependent SER experiments. Moreover, our method yields the best results for speaker-independent SER with existing handcrafted features-based SER approaches.

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A Comparative Study of Different Weighting Schemes on KNN-Based Emotion Recognition in Mandarin Speech

Cited by 26 publications

References 11 publications

Learning Salient Features for Speech Emotion <newline/>Recognition Using Convolutional <newline/>Neural Networks

Learning Salient Features for Speech Emotion <newline/>Recognition Using Convolutional <newline/>Neural Networks

Spatial Temporal Variation Graph Convolutional Networks (STV-GCN) for Skeleton-Based Emotional Action Recognition

Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network

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