English speech emotion recognition method based on speech recognition

Liu, Man

doi:10.1007/s10772-021-09955-4

Cited by 16 publications

(3 citation statements)

References 20 publications

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“…After all, SVM and RF can often achieve high accuracy in the application of binary classification [48]. Although the LIBSVM toolbox is capable of solving multiclassification problems [49], the total classification accuracy of samples is not high because the difference of spectral features between various samples is not big, or the difference of features between samples is further reduced after normalization. Dimensionality reduction reduces the training and testing time of the model, but inevitably leads to the loss of some important information, which is the main reason why the accuracy of dimensionality reduction models is lower than that of non-dimensionality reduction models.…”

Section: Discussionmentioning

confidence: 99%

A Method of Invasive Alien Plant Identification Based on Hyperspectral Images

et al. 2022

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Invasive alien plants (IAPs) are considered to be one of the greatest threats to global biodiversity and ecosystems. Timely and accurate detection technology is needed to identify these invasive plants, helping to mitigate the damage to farmland, fruit trees and woodland. Hyperspectral technology has the potential to identify similar species. However, the challenge remains to simultaneously identify multiple invasive alien plants with similar colors based on image data. The spectral images were collected by a hyperspectral camera with a spectral range of 450–998 nm, and the raw spectra were extracted by Cubert software. First derivative (FD), Savitzky-Golay (SG) smoothing and standard normal variate (SNV) were used to preprocess the raw spectral data, respectively. Then, on the basis of preprocessing, principal component analysis (PCA) and ant colony optimization (ACO) were used for feature dimensionality reduction, and the reduced features were used as input variables for later modeling. Finally, a combination of both dimensionality reduction and non-dimensionality reduction is used for identification using support vector machines (SVM) and random forests (RF). In order to determine the optimal recognition model, a total of 18 combinations of different preprocessing methods, dimensionality reduction methods and classifiers were tested. The results showed that a combination of SG smoothing and SVM achieved a total accuracy (A) of 89.36%, an average accuracy (AA) of 89.39% and an average precision (AP) of 89.54% with a test time of 0.2639 s. In contrast, the combination of SG smoothing, the ACO, and SVM resulted in weaker performance in terms of A (86.76%), AA (86.99%) and AP (87.22%), but with less test time (0.0567 s). The SG-SVM and SG-ACO-SVM models should be selected considering accuracy and time cost, respectively, for recognition of the seven IAPs and background in the wild.

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Section: Discussionmentioning

confidence: 99%

A Method of Invasive Alien Plant Identification Based on Hyperspectral Images

et al. 2022

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“…Based on the results shown in Figure Table 6.a, our proposed method outperforms existing approaches by achieving higher accuracy values with a larger training dataset. Specifically, we compared our method's performance against that of [65], which previously achieved the highest…”

Section: Evaluate Multi-class Classificationmentioning

confidence: 99%

Image Encoded Time Series Classification of Small Datasets: An Innovative Architecture Using Deep Learning Ensembles

Indrasiri,

Kashyap,

Pathirana

2024

Preprint

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Convolutional neural networks (CNNs) are often favored for their strong learning abilities in tackling automatic intelligent models. The classification of time series data streams spans across many applications of intelligent systems. However, the scarcity of effective Machine Learning architectures to handle limited time-series data adversely affects the realization of some crucial applications. In particular, healthcare-related applications are inherently concerned with limited time series datasets. Indeed, building effective artificial intelligence (AI) models for rare diseases using conventional techniques can pose a significant challenge. Utilizing recent advances in deep learning and signal processing techniques, this study introduces a new ensemble deep learning (DL) approach for time series categorization in the presence of limited datasets. Physiological data, such as ECG and voice, are used to demonstrate the functionality of the proposed DL architecture with data obtained from IoT and non-IoT devices. The proposed framework comprises a self-designed deep CNN-LSTM along with ResNet50 and MobileNet transfer learning approaches. The CNN-LSTM architecture includes an enhanced squeeze and excitation block that improves overall performance.This architecture processes time series data transformed into a 3-Channel image structure via improved recurrence plot (RP), Gramian angular field (GAF), and fuzzy recurrence plot (FRP) methods. The proposed model demonstrated superior classification accuracy on the ECG5000 and TESS datasets compared to other state-of-the-art techniques, validating its efficacy for binary and multiclass classification.

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“…While these classification models have made significant contributions to the field of speech emotion recognition, the accuracy of the aforementioned models still requires improvement. 2…”

Section: Introductionmentioning

confidence: 99%

Research on lightweight speech emotion recognition in vehicle noisy environment

Tang,

Qi,

Xie

2024

Advances in Mechanical Engineering

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In order to reduce the incidence of traffic accidents caused by the emotional state of drivers, this study proposes an emotion recognition algorithm based on vehicle noise environment. This algorithm can effectively identify the emotional state of drivers and provide support for further improving their emotions. To address challenges in existing research on speech emotion recognition, such as excessive model parameters, poor generalization, and suboptimal performance in noisy environments, this paper proposes a lightweight network model suitable for small datasets. The model utilizes Power Normalized Cepstral Coefficients (PNCC) as input features, and employs parallel feature extraction layers at different scales. These features are then fed into a feature learning module for in-depth extraction, with the final determination of the driver’s emotional state made by the output layer. Experimental results show that the model achieves an accuracy of 96.08% on the EMO-DB speech dataset. Even in simulated in-vehicle noise environments, the model exhibits high accuracy and robustness. Moreover, compared to other lightweight models, it has fewer training parameters and faster processing speed, making it suitable for deployment on edge devices in mobile applications.

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English speech emotion recognition method based on speech recognition

Cited by 16 publications

References 20 publications

A Method of Invasive Alien Plant Identification Based on Hyperspectral Images

A Method of Invasive Alien Plant Identification Based on Hyperspectral Images

Image Encoded Time Series Classification of Small Datasets: An Innovative Architecture Using Deep Learning Ensembles

Research on lightweight speech emotion recognition in vehicle noisy environment

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