TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Eliminating the negative effect of highly non-stationary environmental noise is a long-standing research topic for speech recognition but remains an important challenge nowadays. To address this issue, traditional unsupervised signal processing methods seem to have touched the ceiling. However, data-driven based supervised approaches, particularly the ones designed with deep learning, have recently emerged as potential alternatives. In this light, we are going to comprehensively summarise the recently developed and most representative deep learning approaches to deal with the raised problem in this article, with the aim of providing guidelines for those who are going deeply into the field of environmentally robust speech recognition. To better introduce these approaches, we categorise them into singleand multi-channel techniques, each of which is specifically described at the front-end, the back-end, and the joint framework of speech recognition systems. In the meanwhile, we describe the pros and cons of these approaches as well as the relationships among them, which can probably benefit future research.

SOLUTION: Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

PROBLEM: A famous biologically inspired hierarchical model firstly proposed by Riesenhuber and Poggio has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of positionand scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental results, we introduce the Memory and Association Mechanisms into the above biologically inspired model. The main motivations of the work are (a) to mimic the active memory and association mechanism and add the 'top down' adjustment to the above biologically inspired hierarchical model and (b) to build up an algorithm which can save the space and keep a good recognition performance. More details of the work could be explained as follows: (1) In objects memorizing process: Our proposed model mimics some characteristics of human's memory mechanism as follows: (a) In our model, one object is memorized by semantic attributes and special image patches (corresponding to episodic memory). The semantic attributes describe each part of the object with clear physical meaning, for example, if eyes and mouths of faces are 'big' or 'small' and so on. One special patch is selected if the value of the corresponding semantic feature is far from average one. The patch should be the most prominent part of the object. (b) In our model, different features (semantic attributes and special patches) of one object are stored in distributed places and the common feature of different objects is saved aggregately, which can learn to classify the difference of similar features of different objects. The similarity thresholds to each object can be learnt when new objects are learnt. (2) In object recognition process: In biological process, the associated recognition including familiarity discrimination and recollective matching. In our proposed model, firstly mimicking familiarity discrimination ('knowing' though the episode), we compare the special patches of candidates with that of saved objects using above mentioned biologically inspired hierarchical model, where the candidates and saved objects have the same prominent semantic features. Then mimicking recollective matching, the comparison results of special patches are combined with semantic feature comparison. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with much less memory requirement.

SOLUTION: Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model

PROBLEM: AbstractThe emergence of collective dynamics in neural networks is a mechanism of the animal and human brain for information processing. In this paper, we develop a computational technique of distributed processing elements, which are called particles. We observe the collective dynamics of particles in a complex network for transductive inference on semi-supervised learning problems. Three actions govern the particles' dynamics: walking, absorption, and generation. Labeled vertices generate new particles that compete against rival particles for edge domination. Active particles randomly walk in the network until they are absorbed by either a rival vertex or an edge currently dominated by rival particles. The result from the model simulation consists of sets of edges sorted by the label dominance. Each set tends to form a connected subnetwork to represent a data class. Although the intrinsic dynamics of the model is a stochastic one, we prove there exists a deterministic version with largely reduced computational complexity; specifically, with subquadratic growth. Furthermore, the edge domination process corresponds to an unfolding map. Intuitively, edges "stretch" and "shrink" according to edge dynamics. Consequently, such effect summarizes the relevant relationships between vertices and uncovered data classes. The proposed model captures important details of connectivity patterns over the edge dynamics evolution, which contrasts with previous approaches focused on vertex dynamics. Computer simulations reveal that our model can identify nonlinear features in both real and artificial data, including boundaries between distinct classes and the overlapping structure of data.

SOLUTION:
Network Unfolding Map by Edge Dynamics Modeling