Full-info Training for Deep Speaker Feature Learning

In recent studies, it has shown that speaker patterns can be learned from very short speech segments (e.g., 0.3 seconds) by a carefully designed convolutional & time-delay deep neural network (CT-DNN) model. By enforcing the model to discriminate the speakers in the training data, frame-level speaker features can be derived from the last hidden layer. In spite of its good performance, a potential problem of the present model is that it involves a parametric classifier, i.e., the last affine layer, which may consume some discriminative knowledge, thus leading to `information leak' for the feature learning. This paper presents a full-info training approach that discards the parametric classifier and enforces all the discriminative knowledge learned by the feature net. Our experiments on the Fisher database demonstrate that this new training scheme can produce more coherent features, leading to consistent and notable performance improvement on the speaker verification task.Comment: Accepted by ICASSP 201

Deep factorization for speech signal

Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors. An intuitive idea is to factorize each speech frame into individual informative factors, though it turns out to be highly difficult. Recently, we found that speaker traits, which were assumed to be long-term distributional properties, are actually short-time patterns, and can be learned by a carefully designed deep neural network (DNN). This discovery motivated a cascade deep factorization (CDF) framework that will be presented in this paper. The proposed framework infers speech factors in a sequential way, where factors previously inferred are used as conditional variables when inferring other factors. We will show that this approach can effectively factorize speech signals, and using these factors, the original speech spectrum can be recovered with a high accuracy. This factorization and reconstruction approach provides potential values for many speech processing tasks, e.g., speaker recognition and emotion recognition, as will be demonstrated in the paper.Comment: Accepted by ICASSP 2018. arXiv admin note: substantial text overlap with arXiv:1706.0177

Research on the Impact of Returnee Executives' Strategic CSR Orientation on Corporate Value

An increasing number of studies have focused on the impact of the increase in the proportion of overseas returnees in enterprise management. How do executives with an overseas background affect corporate value? Based on Upper Echelons Theory, this paper studies the influence of overseas returnees on enterprise value from the perspective of strategic corporate social responsibility. Based on the sample composed of companies listed in Shanghai and Shenzhen stock exchange, the research shows that executives from overseas through strengthening strategic corporate social responsibility, make the enterprise social responsibility incorporated into the development of business strategy, finally promoting the ascension of the enterprise value. Namely, the strategic corporate social responsibility orientation plays an intermediary role between executives from overseas and enterprise value. This study further correlates the characteristics of overseas returnees with the strategic decisions of enterprises, deepens the understanding of the realization mechanism of overseas executives' promotion of corporate value, enriches the research results of factors influencing corporate social responsibility, to improve the active construction of local enterprises' social responsibility by changing the structure of the executive

Colorimetric Detection of Copper Ion Based on Click Chemistry

Two colorimetric assays, lateral flow biosensor (LFB) and hemin/G-Quadruplex DNAzyme-based colorimetric assay, were developed for the detection of copper ion based on click chemistry. Two single-strand DNA (ssDNA) with azide- and alkyne-modified at 3′ and 5′ separately can be linked by the Cu+-catalyzed click chemistry. For hemin/G-Quadruplex DNAzyme-based assay, the two ssDNA fragments linked by Cu+-catalyzed click chemistry could form a complete G-rich sequence that severed as a horse-radish peroxidase. In the presence of hemin and K+, the colorless substrate tetramethyl benzidine (TMB) is catalyzed into a colored product by the G-rich sequence. The concentration of Cu2+ can then be quantitatively analyzed by measuring the color density. For the LFB assay, the two ligated ssDNA fragments could form a sandwich complex between an ssDNA fragment immobilized on gold nanoparticles and another ssDNA fragment on test zone of a biosensor, respectively. The biosensor enables visual detection of copper ion with excellent specificity. In comparison with conventional methods, the present assays are simpler to operate and more cost-effective to use, and so have great potential in point-of-care diagnosis and environmental monitoring