IEEE ICASSP 2020 Virtual Conference May 2020

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  • Deep Clustering With Concrete K-Means

    00:14:05
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    We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential for deep k-means to outperform traditional two-step feature extraction and shallow clust
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  • Self-Training For End-To-End Speech Recognition

    00:14:25
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    We revisit self-training in the context of end-to-end speech recognition. We demonstrate that training with pseudo-labels can substantially improve the accuracy of a baseline model. Key to our approach are a strong baseline acoustic and language model use
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  • Residual Recurrent Neural Network For Speech Enhancement

    00:13:09
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    Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn the temporal correlations acros
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  • Dynamic Temporal Residual Learning For Speech Recognition

    00:11:18
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    Long short-term memory (LSTM) networks have been widely used in automatic speech recognition (ASR). This paper proposes a novel dynamic temporal residual learning mechanism for LSTM networks to better explore temporal dependencies in sequential data. The
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  • Motion Dynamics Improve Speaker-Independent Lipreading

    00:12:09
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    We present a novel lipreading system that improves on the task of speaker-independent word recognition by decoupling motion and content dynamics. We achieve this by implementing a deep learning architecture that uses two distinct pipelines to process moti
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  • Modeling Piece-Wise Stationary Time Series

    00:14:33
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    We consider the problem of modeling piece-wise stationary time series. We propose a new, data-driven technique to automatically identify change-points and learn piece-wise stationary models. We do not assume prior knowledge of the stationary models or the
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  • Hybrid Autoregressive Transducer (Hat)

    00:17:12
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    This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a time-synchronous encoder-decoder model that preserves the modularity of conventional automatic speech recognition systems. The HAT model provides a way to measure the qu
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  • Salient Object Detection Based On Image Bit-Map

    00:13:07
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    In this paper, we propose a novel salient object detection framework, which makes full use of the essential image compression. More specifically, we first compose an intuitive measure of compressibility from JPEG compression, namely bit-map. Then, dependi
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