Deep neural networks (DNNs) have gained remarkable success in speech recognition, partially attributed to the flexibility of DNN models in learning complex patterns of speech signals. This flexibility, however, may lead to serious over-fitting and hence miserable performance degradation in adverse acoustic conditions such as those with high ambient noises. We propose a noisy training approach to tackle this problem: by injecting moderate noises into the training data intentionally and randomly, more generalizable DNN models can be learned. This 'noise injection' technique, although known to the neural computation community already, has not been studied with DNNs which involve a highly complex objective function. The experiments presented in this paper confirm that the noisy training approach works well for the DNN model and can provide substantial performance improvement for DNN-based speech recognition.

An extended-state-observer-based double-loop integral sliding-mode controller for electronic throttle (ET) is proposed by factoring the gear backlash torque and external disturbance to circumvent the parametric uncertainties and nonlinearities. The extended state observer is designed based on a nonlinear model of ET to estimate the change of throttle opening angle and total disturbance. A double-loop integral sliding-mode controller consisting of an inner loop and an outer loop is presented based on the opening angle and opening angle change errors of ET through Lyapunov stability theory. Numerical experiments are conducted using simulation. The results show that the accuracy and the response time of the proposed controller are better than those of the back-stepping and sliding mode control. Index Terms-Double-loop integral sliding-mode control (DLISMC), extended state observer (ESO), electronic throttle (ET) control.

Peer-to-peer (P2P) lending is facing severe information asymmetry problems and depends highly on the internal credit scoring system. This paper provides a novel credit scoring model, which forecasts the probability of default for each applicant and guides the lenders' decision-making in P2P lending. The proposal is expected to improve the existing credit scoring models in P2P lending from two aspects, namely the classifier and the usage of narrative data. We utilize an advanced gradient boosting decision tree technique (i.e., CatBoost) to predict default loans. Moreover, a soft information extraction technique based on keyword clustering is developed to compensate for the insufficient hard credit data. Validated on three real-world datasets, the experimental results demonstrate that variables extracted from narrative data are powerful features, and the utilization of narrative data significantly improves the predictability relative to solely using hard information. The results of sensitivity analysis reveal that CatBoost outperforms the industry benchmark under different cluster numbers of extracted soft information; meanwhile a small number of clusters (e.g., three) is preferred for consideration of model performance, computational cost, and comprehensibility. We finally facilitate a discussion on practical implication and explanatory considerations.

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