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This letter presents a novel approach to quantify the ambient concentrations of fine particulate matter (PM2.5) from multi-weather sensors based on hidden Markov models-oriented statistical methodology. Compared with one current state-of-the-art, the proposed methodology produces a better result, showing potential applications in the existing network of the multi-weather sensors for the PM complementary...
Proper maintenance strategies are very desirable for minimizing the operational and maintenance costs of power systems without sacrificing reliability. Condition-based maintenance has largely replaced time-based maintenance because of the former's potential economic benefits. As offshore substations are often remotely located, they experience more adverse environments, higher failures, and therefore...
This paper takes phonetic information into account for data alignment in text-independent voice conversion. Hidden Markov models are used for representing the phonetic structure of training speech. States belonging to same phoneme are grouped together to form a phoneme cluster. A state mapped codebook based transformation is established using information on the corresponding phoneme clusters from...
Although hidden Markov model based speech synthesis has been proved to have good performance, there are still some factors which degrade the quality of synthesized speech: vocoder, model accuracy and over-smoothing. This paper analyzes these factors separately. Modifications for removing different factors are proposed. Experimental results show that over-smoothing in frequency domain mainly affect...
This paper describes a novel method for text-independent voice conversion using improved state mapping. HMM is used for representing the phonetic structure of training speech. Centroids of the common phonemes between source and target speech are utilized as phonetic anchors while establishing a mapping between acoustic spaces of source and target speakers. These phonetic anchors and weighted linear...
Voice conversion has become more and more important in speech technology, but most of current works have to use parallel utterances of both source and target speaker as the training corpus, which limits the application of the technology. In the paper, we propose a new method of text- independent voice conversion which uses non-parallel corpus for the training. The hidden Markov model (HMM) is used...
In speech recognition system, recognition rate is always influenced by noise because training and recognition models are often mismatch in noisy environments. In this paper, we present a recognition system based on speech enhancement. First, noisy speech is enhanced by a filter composed of morphology and wavelet, then enhanced signals are sent into recognition system based on hidden Markov model,...
The conventional HMM-based speech synthesis system (HTS) has encountered two over-smoothing problems in both time domain and frequency domain. To resolve the two problems, the paper presents a new HTS framework using both continuous HMMs and discrete HMMs. By the replacement of spectral envelope represented by continuous Gaussian distribution with that represented by discrete codevector, the over-smoothing...
In this paper, an efficient despeckling algorithm is proposed based on the hidden-state Markov random field (MRF) and the hidden Markov tree (HMT) in the wavelet domain for synthetic aperture radar (SAR) image. The minimum mean square error (MMSE) despeckling technique without the log-transform is fused in the algorithm. This algorithm also employs a new hidden Markov half tree model, which improves...
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