Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition

Wang, Jianrong; Wu, Tong; Wang, Shanyu; Yu, Mo; Fang, Qiang; Zhang, Ju; Liu, Li

doi:10.1109/icpr48806.2021.9413218

Cited by 4 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recommender [201] Regressionmodels [142,174] RepTree [203] review [121,138,188,190,204,248] RFM [207] R-GCN [242] RNN [80] Robotandpressuremeasurements [146] SEM [239] siamesenetwork [46,75,79,156] SSD [35,92] Survey [53,127,143,150,162,181,191,196,210,219,221] Survey:kanomodel [139] SVM [64,87,153,179] SVM.REPTree [172] SVP [222] UCB [233] VAR [207] VGG-IE [65] Viola-Jones [148] word2vec [98,102,167] word2vecSVMperf [180] XGBoost [176] Table 6<...…”

Section: R-cnn [59]mentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

View full text Add to dashboard Cite

Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.

show abstract

Section: R-cnn [59]mentioning

confidence: 99%

“…Summary of purposes with references. Sentimentanalysis [42,44,[169][170][171][172][173][174][175][176][177][178][179][180] Virtualfitting [32,[129][130][131][132][133][134][135][136][138][139][140][141][142][143][144][145][146][149][150][151][152] Table 7. Summary of databases with references.…”

Section: R-cnn [59]mentioning

confidence: 99%

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…Feature Extraction. Traditional studies use pixel-based [186], shape-based [187], [188], and hybrid-based [189], [190], [191], [192], [193] approaches to extract the visual feature. However, these methods are not only sensitive to image illumination change, lip deformation, and rotation but also cannot extract automatically.…”

Section: Automatic Lip Readingmentioning

confidence: 99%

Multi-Modal Fusion Emotion Recognition Method of Speech Expression Based on Deep Learning

Liu

Wang

et al. 2021

Front. Neurorobot.

View full text Add to dashboard Cite

The redundant information, noise data generated in the process of single-modal feature extraction, and traditional learning algorithms are difficult to obtain ideal recognition performance. A multi-modal fusion emotion recognition method for speech expressions based on deep learning is proposed. Firstly, the corresponding feature extraction methods are set up for different single modalities. Among them, the voice uses the convolutional neural network-long and short term memory (CNN-LSTM) network, and the facial expression in the video uses the Inception-Res Net-v2 network to extract the feature data. Then, long and short term memory (LSTM) is used to capture the correlation between different modalities and within the modalities. After the feature selection process of the chi-square test, the single modalities are spliced to obtain a unified fusion feature. Finally, the fusion data features output by LSTM are used as the input of the classifier LIBSVM to realize the final emotion recognition. The experimental results show that the recognition accuracy of the proposed method on the MOSI and MELD datasets are 87.56 and 90.06%, respectively, which are better than other comparison methods. It has laid a certain theoretical foundation for the application of multimodal fusion in emotion recognition.

show abstract