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.

Deep learning systems have been widely used as well as an important tool for automatic classification. However, in many applications it is not possible to apply full convolutional networks to a particular domain. In this work, we show how to transfer information from a different and more general domain, such as vision. We demonstrate here that a deep-learning system can be applied to visual information retrieval in a semantic domain, where it performs semantic categorization and can also recognize specific objects and items. We propose a deep-learning system to process visual concepts from a semantic domain in a 3D world and demonstrate how this can be applied to real-world datasets. We demonstrate our system on a dataset of medical images obtained from radiology.

Learning Gaussian Process Models by Integrating Spatial & Temporal Statistics

Learning Latent Representations with Pairwise Sparse Coding

Generating a Robust Multimodal Corpus for Robust Speech Recognition

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  • A Generative framework for Neural Networks in Informational and Personal Exploration

    A Novel Approach for Spatial-Temporal Image Denoising and Background Texture Synthesis Based on Convolutional Neural NetworkDeep learning systems have been widely used as well as an important tool for automatic classification. However, in many applications it is not possible to apply full convolutional networks to a particular domain. In this work, we show how to transfer information from a different and more general domain, such as vision. We demonstrate here that a deep-learning system can be applied to visual information retrieval in a semantic domain, where it performs semantic categorization and can also recognize specific objects and items. We propose a deep-learning system to process visual concepts from a semantic domain in a 3D world and demonstrate how this can be applied to real-world datasets. We demonstrate our system on a dataset of medical images obtained from radiology.


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