Abstract--In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multisource fusionscheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while the user is driving. A Deep Neural Network (DNN) is trained to extract acoustic features and make a speech classification. Based on the posterior probabilities obtained from DNN, the vehicle information including the speed and direction of vehicle is applied to choose the suitable parameter from a pair of sensitivity values for the KWS system. The experimental results show that the KWS system with the proposed multi-source fusion scheme can achieve better performances in term of precision rate, recall rate, and mean square error compared to the system without it. I. INTRODUCTION Keyword Spotting (KWS) System, also known as wakeword detection,refers to the task of detecting specified keyword from a continuous stream of audio provided by the users [1]. Keyword Spotting has been an active research area in speech recognition for decades, and widely used in numerous applications.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. I have no idea what to even say about the Opportunity rover. I'm not sure that the amazing people at JPL do, either.

As journalists face increased layoffs despite the growing appetite for up-to-the-minute, timely news, a new trend has quietly been disrupting the news industry. News organizations are increasingly turning toward artificial intelligence (AI) for production, using a variety of new automated systems to pump out content with minimal need for direct human input. According to a report by The New York Times, Bloomberg News relies on a system called Cyborg to produce about a third of its articles. Most of Cyborg's output takes the form of company earnings reports that are rife with percentages, charts, and other financial data that can be crunched down into a news story quickly and accurately. Increasingly, major news agency like Reuters and Associated Press, along with a number of newspapers such as Washington Post and Los Angeles Times, are using algorithms to crunch out news on everything from local minor league sports games to earthquakes.

Neural architecture search has been shown to hold great promise towards the automation of deep learning. However in spite of its potential, neural architecture search remains quite costly. To this point, we propose a novel gradient-based framework for efficient architecture search by sharing information across several tasks. We start by training many model architectures on several related (training) tasks. When a new unseen task is presented, the framework performs architecture inference in order to quickly identify a good candidate architecture, before any model is trained on the new task. At the core of our framework lies a deep value network that can predict the performance of input architectures on a task by utilizing task meta-features and the previous model training experiments performed on related tasks. We adopt a continuous parametrization of the model architecture which allows for efficient gradient-based optimization. Given a new task, an effective architecture is quickly identified by maximizing the estimated performance with respect to the model architecture parameters with simple gradient ascent. It is key to point out that our goal is to achieve reasonable performance at the lowest cost. We provide experimental results showing the effectiveness of the framework despite its high computational efficiency.

So read the T-shirt sported by Ben Recht, a professor at the University of California, Berkeley, as he collected an award at the Neural Information Processing Systems (NIPS) conference this week. Dr Recht, pictured above in lecture mode, was protesting against the flood of corporate money pouring into NIPS, aping the words Kurt Cobain wrote on a T-shirt when he appeared on the cover of Rolling Stone in 1992. "It's not an academic conference anymore," Dr Recht says wistfully, perched in the Californian sun on the steps of the Long Beach Convention Centre. He complains that folk would rather go to corporate-sponsored parties these days (Intel's featured Flo Rida, a rapper), than poster sessions. AI, it seems, is the new rock and roll.

Quantum Clustering is a powerful method to detect clusters in data with mixed density. However, it is very sensitive to a length parameter that is inherent to the Schr\"odinger equation. In addition, linking data points into clusters requires local estimates of covariance that are also controlled by length parameters. This raises the question of how to adjust the control parameters of the Schr\"odinger equation for optimal clustering. We propose a probabilistic framework that provides an objective function for the goodness-of-fit to the data, enabling the control parameters to be optimised within a Bayesian framework. This naturally yields probabilities of cluster membership and data partitions with specific numbers of clusters. The proposed framework is tested on real and synthetic data sets, assessing its validity by measuring concordance with known data structure by means of the Jaccard score (JS). This work also proposes an objective way to measure performance in unsupervised learning that correlates very well with JS.

In this paper we study the generalisation capabilities of fully-connected neural networks trained in the context of time series forecasting. Time series do not satisfy the typical assumption in statistical learning theory of the data being i.i.d. samples from some data-generating distribution. We use the input and weight Hessians, that is the smoothness of the learned function with respect to the input and the width of the minimum in weight space, to quantify a network's ability to generalise to unseen data. While such generalisation metrics have been studied extensively in the i.i.d. setting of for example image recognition, here we empirically validate their use in the task of time series forecasting. Furthermore we discuss how one can control the generalisation capability of the network by means of the training process using the learning rate, batch size and the number of training iterations as controls. Using these hyperparameters one can efficiently control the complexity of the output function without imposing explicit constraints.

We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly "harder" images than those found in the original test sets.

We propose a new policy iteration theory as an important extension of soft policy iteration and Soft Actor-Critic (SAC), one of the most efficient model free algorithms for deep reinforcement learning. Supported by the new theory, arbitrary entropy measures that generalize Shannon entropy, such as Tsallis entropy and Renyi entropy, can be utilized to properly randomize action selection while fulfilling the goal of maximizing expected long-term rewards. Our theory gives birth to two new algorithms, i.e., Tsallis entropy Actor-Critic (TAC) and Renyi entropy Actor-Critic (RAC). Theoretical analysis shows that these algorithms can be more effective than SAC. Moreover, they pave the way for us to develop a new Ensemble Actor-Critic (EAC) algorithm in this paper that features the use of a bootstrap mechanism for deep environment exploration as well as a new value-function based mechanism for high-level action selection. Empirically we show that TAC, RAC and EAC can achieve state-of-the-art performance on a range of benchmark control tasks, outperforming SAC and several cutting-edge learning algorithms in terms of both sample efficiency and effectiveness.

The concordance correlation coefficient (CCC) is one of the most widely used reproducibility indices, introduced by Lin in 1989. In addition to its extensive use in assay validation, CCC serves various different purposes in other multivariate population-related tasks. For example, it is often used as a metric to quantify an inter-rater agreement. It is also often used as a performance metric for prediction problems. In terms of the cost function, however, there has been hardly any attempt to design one to train the predictive deep learning models. In this paper, we present a family of lightweight cost functions that aim to also maximise CCC, when minimising the prediction errors. To this end, we first reformulate CCC in terms of the errors in the prediction; and then as a logical next step, in terms of the sequence of the fixed set of errors. To elucidate our motivation and the results we obtain through these error rearrangements, the data we use is the set of gold standard annotations from a well-known database called `Automatic Sentiment Analysis in the Wild' (SEWA), popular thanks to its use in the latest Audio/Visual Emotion Challenges (\textsc{AVEC'17} and \textsc{AVEC'18}). We also present some new and interesting mathematical paradoxes we have discovered through this CCC reformulation endeavour.