Generating a Robust Multimodal Corpus for Robust Speech Recognition


Generating a Robust Multimodal Corpus for Robust Speech Recognition – Person recognition is a vital task in many computer-based applications, but human performance is typically too poor to be considered a benchmark. However, it’s very important to consider the role of the human to make the decisions regarding what person to recognize. This paper presents a novel approach for face recognition in action videos, which is based on a deep network. The network is trained for a multi-dimensional space (with both a facial and a visual input), which is capable to capture the human’s face attributes. Experiments show that the proposed model is capable of recognising human expressions (including the facial-expression similarity level) of human. Moreover, it makes it possible to identify people that have been described as being similar to the human. Therefore, the proposed approach may be useful to users of action-based video games.

In this paper, we propose a novel approach to automatic segmentation of the brain of Alzheimer’s (AD) by measuring magnetic resonance imaging (MRI) without hand-modeled data. The proposed method, which consists of a self-learning system, a self-learning system and a learned discriminative system, has been developed in two different phases: an automatic segmentation task where a human is trained from a small set of annotated images, and a segmentation task which involves using multiple annotated samples and simultaneously learning a discriminative representation from each image. The segmentation task is designed to mimic a human’s brain segmentation task and provides a good comparison of the performance between the three systems. Our results compare the performance of the two systems (differently trained on the same images) and reveal the key differences between them (similarly trained on a few annotated regions and the human). This demonstrates the importance of distinguishing between different segmentation approaches for Alzheimer’s disease (AD) recognition and other cognitive diseases.

Interactive Stochastic Learning

Semantic Machine Meet Benchmark

Generating a Robust Multimodal Corpus for Robust Speech Recognition

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  • A study of the effect of different covariates in the estimation of the multi-point ensemble sigma coefficient

    Image quality assessment by non-parametric generalized linear modelingIn this paper, we propose a novel approach to automatic segmentation of the brain of Alzheimer’s (AD) by measuring magnetic resonance imaging (MRI) without hand-modeled data. The proposed method, which consists of a self-learning system, a self-learning system and a learned discriminative system, has been developed in two different phases: an automatic segmentation task where a human is trained from a small set of annotated images, and a segmentation task which involves using multiple annotated samples and simultaneously learning a discriminative representation from each image. The segmentation task is designed to mimic a human’s brain segmentation task and provides a good comparison of the performance between the three systems. Our results compare the performance of the two systems (differently trained on the same images) and reveal the key differences between them (similarly trained on a few annotated regions and the human). This demonstrates the importance of distinguishing between different segmentation approaches for Alzheimer’s disease (AD) recognition and other cognitive diseases.


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