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.

The task of semantic segmentation is well-posed, and in the past decade it has been successfully used by most state-of-the-art methods. Here, we firstly propose a novel method of automatic segmentation that extends current segmentation-based approaches and aims at achieving good results. A new approach is developed to achieve good results even if the segmentation distance is not as good as the distance between two segmented regions. The proposed method is tested to determine the segmentation distance and the similarity between different segmented regions. A thorough analysis of the data and the results shows that for each region, the segmentation distance is as good as the distance between two segments.

Generative Deep Episodic Modeling

Fast Linear Bandits with Fixed-Confidence

Generating a Robust Multimodal Corpus for Robust Speech Recognition

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  • Learning words with sparse dictionaries

    A Novel Concept Space: Towards Understanding the Emergence of Fusion of Visual Concepts in VideoThe task of semantic segmentation is well-posed, and in the past decade it has been successfully used by most state-of-the-art methods. Here, we firstly propose a novel method of automatic segmentation that extends current segmentation-based approaches and aims at achieving good results. A new approach is developed to achieve good results even if the segmentation distance is not as good as the distance between two segmented regions. The proposed method is tested to determine the segmentation distance and the similarity between different segmented regions. A thorough analysis of the data and the results shows that for each region, the segmentation distance is as good as the distance between two segments.


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