With the demand on providing automatic speech recognition (ASR) systems for many markets the question of porting an ASR system to a new language is of practical interest. To cope with this task the adaptation of hidden Markov models (HMM) is seen as a key step to transfer the models from a source to a target language. In this work we introduce a novel adaptation scheme for semi-continuous HMMs ...

This paper explores the use of the homotopy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorithm to trace the local maximum of the likelihood function for HMMs from full weight on the labeled data to full weight on the unlabeled data. We present an experimental analysis of different techniques for choosing the b...

due to the effective role of markov models in customer relationship management (crm), there is a lack of comprehensive literature review which contains all related literatures. in this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. one hundred articles were identified and reviewed to find direct relevance for applying markov models...

This paper investigates a projection-based likelihood meaure that improves speech recognition performance in noisy environment. The projection-based likelihood measure is modi ed to give the weighting and projection e ect and to reduce computational complexity. It is evaluated in sub-model based word recognition using semi-continuous hidden Markov model with speaker independent mode. Experiment...

We have extended Morfessor Baseline, which is a well-known method for unsupervised morphological segmentation, to semi-supervised learning. As submission to Morpho Challenge 2010, we provide results from three methods: The first one is based on the unsupervised algorithm, but includes a weight parameter that can be used to control the amount of segmentation. The second one applies the semisuper...

As one of the most powerful smoothing techniques, deleted interpolation has been widely used in both discrete and semi-continuous hidden Markov model (HMM) based speech recognition systems. For continuous HMMs, most smoothing techniques are carried out on the parameters themselves such as Gaussian mean or covariance parameters. In this paper, we propose to smooth the probability density values ...

This paper deals with a subword-unit-based isolated word recognition system with enhanced speaker-independency. The subword is defined as a part of word whose central portion has rather stationary or time-invariant short-time spectra with its portions near its ends having rapidly varying short-time spectra. In this system each isolated word is decomposed into a sequence of subwords, each of whi...

Abstract: It is difficult for the existing anomaly detection methods to distinguish the burst of normal traffic from the anomalous traffic in a large-scale web site. This paper extends the current Hidden semi-Markov Model (HsMM) that is used for single sequence of observations to the HsMM with multiple sequences of observations, and proposes a detection approach on user behaviors based on this ...

We define the crouching Dirichlet, hidden Markov model (CDHMM), an HMM for partof-speech tagging which draws state prior distributions for each local document context. This simple modification of the HMM takes advantage of the dichotomy in natural language between content and function words. In contrast, a standard HMM draws all prior distributions once over all states and it is known to perfor...