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h5 指数:30
h5 中位数:44

h5 指数是指在过去整整 5 年中所发表文章的 h 指数。h 指在 2017-2021 年间发表的 h 篇文章每篇至少都被引用过 h 次的最大值。隐藏

出版物的 h5 中位数，是指出版物的 h5 指数所涵盖的所有文章获得的引用次数的中位值。隐藏

#16 Acoustics & Sound

标题/作者	引用次数	年份
Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers MB Akçay, K Oğuz Speech Communication 116, 56-76	232	2020
An overview of voice conversion systems SH Mohammadi, A Kain Speech Communication 88, 65-82	202	2017
Deep learning for minimum mean-square error approaches to speech enhancement A Nicolson, KK Paliwal Speech Communication 111, 44-55	80	2019
The sound of confidence and doubt X Jiang, MD Pell Speech Communication 88, 106-126	80	2017
Empirical Mode Decomposition for adaptive AM-FM analysis of Speech: A Review R Sharma, L Vignolo, G Schlotthauer, MA Colominas, HL Rufiner, ... Speech Communication 88, 39-64	74	2017
Investigation of different speech types and emotions for detecting depression using different classifiers H Jiang, B Hu, Z Liu, L Yan, T Wang, F Liu, H Kang, X Li Speech Communication 90, 39-46	71	2017
Hybrid convolutional neural networks for articulatory and acoustic information based speech recognition V Mitra, G Sivaraman, H Nam, C Espy-Wilson, E Saltzman, M Tiede Speech Communication 89, 103-112	71	2017
Error detection and accuracy estimation in automatic speech recognition using deep bidirectional recurrent neural networks A Ogawa, T Hori Speech Communication 89, 70-83	67	2017
End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition D Palaz, M Magimai-Doss, R Collobert Speech Communication 108, 15-32	67	2019
Investigating different representations for modeling and controlling multiple emotions in DNN-based speech synthesis J Lorenzo-Trueba, GE Henter, S Takaki, J Yamagishi, Y Morino, Y Ochiai Speech Communication 99, 135-143	66	2018
Single-channel multi-talker speech recognition with permutation invariant training Y Qian, X Chang, D Yu Speech Communication 104, 1-11	63	2018
Epoch extraction from emotional speech using single frequency filtering approach SR Kadiri, B Yegnanarayana Speech Communication 86, 52-63	56	2017
Speech emotion recognition based on DNN-decision tree SVM model L Sun, B Zou, S Fu, J Chen, F Wang Speech Communication 115, 29-37	52	2019
Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN, MS-CNN and LLD-RNN Z Yao, Z Wang, W Liu, Y Liu, J Pan Speech Communication 120, 11-19	51	2020
Automatic lexical stress and pitch accent detection for L2 English speech using multi-distribution deep neural networks K Li, S Mao, X Li, Z Wu, H Meng Speech Communication 96, 28-36	47	2018
Phoneme-to-viseme mappings: the good, the bad, and the ugly HL Bear, R Harvey Speech Communication 95, 40-67	42	2017
Speech-driven animation with meaningful behaviors N Sadoughi, C Busso Speech Communication 110, 90-100	41	2019
Automatic speech emotion recognition using an optimal combination of features based on EMD-TKEO L Kerkeni, Y Serrestou, K Raoof, M Mbarki, MA Mahjoub, C Cleder Speech Communication 114, 22-35	40	2019
Updating the Silent Speech Challenge benchmark with deep learning Y Ji, L Liu, H Wang, Z Liu, Z Niu, B Denby Speech Communication 98, 42-50	38	2018
Speaker recognition from whispered speech: A tutorial survey and an application of time-varying linear prediction V Vestman, D Gowda, M Sahidullah, P Alku, T Kinnunen Speech Communication 99, 62-79	37	2018

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