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| 1 | Multi-scale discrepancy adversarial network for cross-corpus speech emotion recognition显示文摘Background One of the most critical issues in human-computer interaction applications is recognizing human emotions based on speech.In recent years,the challenging problem of cross-corpus speech emotion recognition(SER)has generated extensive research.Nevertheless,the domain discrepancy between training data and testing data remains a major challenge to achieving improved system performance.Methods This paper introduces a novel multi-scale discrepancy adversarial(MSDA)network for conducting multiple timescales domain adaptation for cross-corpus SER,i.e.,integrating domain discriminators of hierarchical levels into the emotion recognition framework to mitigate the gap between the source and target domains.Specifically,we extract two kinds of speech features,i.e.,handcraft features and deep features,from three timescales of global,local,and hybrid levels.In each timescale,the domain discriminator and the feature extrator compete against each other to learn features that minimize the discrepancy between the two domains by fooling the discriminator.Results Extensive experiments on cross-corpus and cross-language SER were conducted on a combination dataset that combines one Chinese dataset and two English datasets commonly used in SER.The MSDA is affected by the strong discriminate power provided by the adversarial process,where three discriminators are working in tandem with an emotion classifier.Accordingly,the MSDA achieves the best performance over all other baseline methods.Conclusions The proposed architecture was tested on a combination of one Chinese and two English datasets.The experimental results demonstrate the superiority of our powerful discriminative model for solving cross-corpus SER. | Wanlu ZHENG Wenming ZHENG Yuan ZONG | 2021 | Virtual Reality & Intelligent Hardware2021，3，1： | 2 |
| 2 | Efficient hydrogengas production from cassava and food waste by a two-step processof dark fermentation and photo- fermentation显示文摘 | Zong Wenming Yu Ruisong Zhang Peng | 2009 | Biomass andBioeneiiy2009，33，10： | 1 |
| 3 | Characteristics of a new photosynthetic bacterial strain for hydrogen production and its application in wastewater treatment显示文摘 | Yongzhen Tao Yanling He Yongqiang Wu Fanghua Liu Xinfeng Li Wenming Zong Zhihua Zhou | 2008 | International Journal of Hydrogen Energy2008，，3： | 1 |
| 4 | Organic Geochemical Characteristics of Source Rocks of Hongshuizhuang Formation in the Mengjiawopu Section of Lingyuan-Ningcheng Basin in North China显示文摘Through the regional geological survey,outcrop section measurement and other field geological work,a block of black shale is found at the outcrop of Mengjiawopu Section of Hongshuizhuang Formation in Jixian County,and organic geochemical source rock analysis and evaluation methods are used to study the source rocks in Hongshuizhuang Formation.The set of source rocks is mainly gray-black silty mudstone and gray-black shale,with a cumulative thickness of 78 m;the total organic matter(TOC)is generally high,with an average content of 1.35%,of which good and rich source rock samples account for 86%.A combined analysis of the stable carbon isotope of kerogen,the correlation between Pr/nC17 and Ph/nC18,and the ratio of saturated hydrocarbon to aromatic hydrocarbon identifies that the organic matter is mainly TypeⅡkerogen;the equivalent vitrinite reflectance R o of organic matter is from 1.68%to 1.94%.R c,the correlation of the methylphenanthrene index(MPI)against the vitrinite reflectance is from 1.63%to 1.81%,with an average of 1.71%,and the Odd/Even Predominance(OEP)value is between 0.87 and 1.11,both indicating that the source rocks of the Hongshuizhuang Formation are at the stage of high to over maturation.In summary,the set of source rocks in Hongshuizhuang Formation is characterized by high organic matter content,good kerogen types,and high to over maturation stage. | Quan Xiong Youjun Tang Wenming Zong Shouliang Sun Daxiang He Feng Wang | 2019 | Open Journal of Yangtze Oil and Gas2019，4，3： | 0 |
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