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Monitoring of dynamic industrial process has been increasingly important due to more and more strict safety and reliability requirements. Popular methods like time lagged arrangement-based and subspace-based approaches exhibit good performance in fault detection, however, they suffer from difficulty in accurately isolating faulty variables and diagnosing fault types. To alleviate this difficulty,...
Activity sensing has become a key technology for many ubiquitous applications, such as exercise monitoring and elder care. Most traditional approaches track the human motions and perform activity recognition based on the waveform matching schemes in the raw data representation level. In regard to the complex activities with relatively large moving range, they usually fail to accurately recognize these...
We propose to use a feature representation obtained by pairwise learning in a low-resource language for query-by-example spoken term detection (QbE-STD). We assume that word pairs identified by humans are available in the low-resource target language. The word pairs are parameterized by a multi-lingual bottleneck feature (BNF) extractor that is trained using transcribed data in high-resource languages...
Recently, deep and/or recurrent neural networks (DNNs/RNNs) have been employed for voice conversion, and have significantly improved the performance of converted speech. However, DNNs/RNNs generally require a large amount of parallel training data (e.g., hundreds of utterances) from source and target speakers. It is expensive to collect such a large amount of data, and impossible in some applications,...
Adaptability and controllability are the major advantages of statistical parametric speech synthesis (SPSS) over unit-selection synthesis. Recently, deep neural networks (DNNs) have significantly improved the performance of SPSS. However, current studies are mainly focusing on the training of speaker-dependent DNNs, which generally requires a significant amount of data from a single speaker. In this...
Polyphone disambiguation in Mandarin Chinese aims to pick up the correct pronunciation from several candidates for a polyphonic character. It serves as an essential component in human language technologies such as text-to-speech synthesis. Since the pronunciation for most polyphonic characters can be easily decided from their contexts in the text, in this paper, we address the polyphone disambiguation...
A large number of videos are generated and uploaded to video websites (like youku, youtube) every day and video websites play more and more important roles in human life. While bringing convenience, the big video data raise the difficulty of video summarization to allow users to browse a video easily. However, although there are many existing video summarization approaches, the key frames selected...
Land cover change detection has long been a hot field in polarimetric synthetic aperture radar (SAR) applications. In certain cases, we care not only the changed areas but also from which type to another. This paper presents a supervised urban land cover change types identification method using a series of polarimetric descriptors from SAR observables and polarimetric decomposition. The normalized...
This paper studies unsupervised acoustic units discovery from unlabelled speech data. This task is usually approached by two steps, i.e., partitioning speech utterances into segments and clustering these segments into subword categories. In previous approaches, the clustering step usually assumes the number of subword units are known beforehand, which is unreasonable for zero-resource languages. Moreover,...
This paper presents a deep neural network-conditional random field (DNN-CRF) system with multi-view features for sentence unit detection on English broadcast news. We proposed a set of multi-view features extracted from the acoustic, articulatory, and linguistic domains, and used them together in the DNN-CRF model to predict the sentence boundaries. We tested the accuracy of the multi-view features...
A new data-driven system identification method, called KL-GP, is proposed for spatiotemporal system. It combines Karhunen-Loève (KL) decomposition and Gaussian process (GP) models. As the nonlinear spatial-temporal spatiotemporal system has strong spatiotemporal characteristics, KL decomposition with good characteristics is employed for time/space separation and dimension reduction. Then the spatiotemporal...
This brief develops a novel just-in-time (JIT) learning-based soft sensor for modeling of industrial processes. The recorded data is assumed to exhibit non-Gaussian signal components, which are extracted by a non-Gaussian regression (NGR) technique. Unlike previous work on JIT modeling which uses distance-based similarity measure for local modeling, this brief introduces a new similarity measure for...
Face sketch-to-photo synthesis has attracted increasing attention in recent years for its useful applications on both digital entertainment and law enforcement. Although great progress has been made, previous methods only work on face sketches with rich textures which are not easily to obtain. In this paper, we propose a robust algorithm for synthesizing a face photo from a simple line drawing that...
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