UNLABELLED The advent of high-density, high-volume genomic data has created the need for tools to summarize large datasets at multiple scales. HMMSeg is a command-line utility for the scale-specific segmentation of continuous genomic data using hidden Markov models (HMMs). Scale specificity is achieved by an optional wavelet-based smoothing operation. HMMSeg is capable of handling multiple data...

-—— In speaker-independent speech recognition. the disadvantage of the most di行used technology (HMMs.or Hidden Markov models)is not only the need of m any m ore training sam ples,but also long train tim e requirem ent. This PaPer describes the use of Biom im etic pattern recognition(BPR)in recognizing some mandarin continuous speech in a speaker-independent m anner. A speech database was develo...

The technique of hidden Markov models has been established as one of the most successful methods applied to the problem of speech recognition. However, its performance is considerably degraded when the speech signal is contaminated by noise. This work presents a technique which improves the performance of hidden Markov models when these models are used in different noise conditions during the s...

In [1], we described how to improve Semi-Continuous Density Hidden Markov Models (SC-HMMs) to be as fast as Continuous Density HMMs (CD-HMMs), whilst outperforming them on large vocabulary recognition tasks with context independent models. In this paper, we extend our work with SC-HMMs to context dependent modelling. We propose a novel node splitting criterion in an approach with phonetic decis...

two statistical downscaling models, the non-homogeneous hidden markov model (nhmm) and the statistical down–scaling model (sdsm) were used to generate future scenarios of both mean and extremes in the tarim river basin,which were based on nine combined scenarios including three general circulation models (gcms) (csiro30, echam5,and gfdl21) predictor sets and three special report on emission sce...

In this article, we present a novel mechanism by which more precise voiceprints can be constructed in a typical text-dependent speaker veri cation system based on a continuous density hidden Markov model (HMM). Typical voiceprints (speaker-dependent HMMs) are rst trained using a subscriber's enrollment data. The resulting models are then restructured to permit a modeling of sub-state behavior. ...

Abstract Continuous-time regime-switching models are a very popular class of for financial applications. In this work the so-called signal-to-noise matrix is introduced hidden Markov where switching driven by an unobservable chain. Its relations to filtering, i.e. state estimation chain given available observations, and portfolio optimization investigated. A convergence result filter derived: T...