Predicting Speech Ambiguity of Linguistic Contexts with Multi-Tensor Networks


Predicting Speech Ambiguity of Linguistic Contexts with Multi-Tensor Networks – In this paper, we present a new framework for speech understanding in natural language, based on the use of a deep neural network (DNN) to recognize speech phrases. The system first learns a sequence of words to encode the phrase into a vector space using a multi-level feature representation. Next, it uses a neural network to capture the semantic similarity between words, based on the word embedding space and their relation to sentence descriptions. A DNN trained on the word embedding space can recognize both sentences and phrases with higher precision than that provided for by state-of-the-art deep learning methods. Finally, we use these system to develop and test a speech recognition system able to recognize phrases like I’m just a human and I speak English and This is a question. The evaluation of the system shows that it correctly identifies more than 90% of phrases with positive speech-related annotations.

Deep convolutional neural networks (CNNs) are widely used in many visual-text classification tasks, particularly for visual-text retrieval and scene summarization. It is well known that convolutional neural networks (CNN) provide good performance on multiple tasks at different times, even when the task is long. However, deep CNNs are rarely used to solve different tasks. This makes it hard to directly solve large-scale tasks. In this paper, we propose to learn a CNN-CNN model that learns the embedding for visual-text. Specifically, we first estimate the visual-text retrieval task using the ConvNet. Then, we construct a CNN for learning the retrieval and summarization tasks using the LSTM model. Finally, we use the training set in an iterative manner, as it involves the training set and the summarization task. Since the task itself is a complex task, we present a novel model to learn the embedding in the convolutional neural networks. We demonstrate the power of our neural embedding learning approach, which can effectively reduce the computational complexity significantly.

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Predicting Speech Ambiguity of Linguistic Contexts with Multi-Tensor Networks

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    Video Summarization with Deep Feature AggregationDeep convolutional neural networks (CNNs) are widely used in many visual-text classification tasks, particularly for visual-text retrieval and scene summarization. It is well known that convolutional neural networks (CNN) provide good performance on multiple tasks at different times, even when the task is long. However, deep CNNs are rarely used to solve different tasks. This makes it hard to directly solve large-scale tasks. In this paper, we propose to learn a CNN-CNN model that learns the embedding for visual-text. Specifically, we first estimate the visual-text retrieval task using the ConvNet. Then, we construct a CNN for learning the retrieval and summarization tasks using the LSTM model. Finally, we use the training set in an iterative manner, as it involves the training set and the summarization task. Since the task itself is a complex task, we present a novel model to learn the embedding in the convolutional neural networks. We demonstrate the power of our neural embedding learning approach, which can effectively reduce the computational complexity significantly.


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