We present an evaluation of the perception of foreign-accented natural and synthetic speech in comparison to accent-reduced synthetic speech. Our method for foreign accent conversion is based on mapping of Hidden Semi-Markov Model states between accented and non-accented voice models and does not need an average voice model of accented speech. We employ the method on recorded data of speakers w...

Tracking the movements of a target based on limited observations plays a role in many interesting applications. Existing probabilistic tracking techniques have shown considerable success but the majority assume simplistic motion models suitable for short-term, local motion prediction. Agent movements are often governed by more sophisticated mechanisms such as a goal-directed pathplanning algori...

We attack a woefully under-explored language genre—lyrics in music—introducing a novel hidden Markov model based method for completely unsupervised identifica-tion of rhyme schemes in hip hop lyrics, which to the best of our knowledge, is the first such effort. Unlike previous approaches that use supervised or semi-supervised approaches for the task of rhyme scheme identification, our model doe...

This paper describes a style adaptation technique using hidden semi-Markov model (HSMM) based maximum likelihood linear regression (MLLR). The HSMM-based MLLR technique can estimate regression matrices for affine transform of mean vectors of output and state duration distributions which maximize likelihood of adaptation data using EM algorithm. In this study, we apply this adaptation technique ...

Structural information about a document is essential for structured query processing, indexing, and retrieval. A document page can be partitioned into a hierarchy of homogeneous regions such as columns, paragraphs, etc.; these regions are called physical components, and define the physical layout of the page. In this paper we develop a class of models for the physical layouts of technical paper...

In this paper, hidden semi-Markov model (HSMM) is introduced into intrusion detection. Hidden Markov model (HMM) has been applied in intrusion detection systems several years, but it has a major weakness: the inherent duration probability density of a state in HMM is exponential, which may be inappropriate for the modeling of audit data of computer systems. We can handle this problem well by de...

Semi-Markov and modulated renewal processes provide a large class of multi-state models which can be used for analysis of longitudinal failure time data. In biomedical applications, models of this kind are often used to describe evolution of a disease and assume that patient may move among a finite number of states representing different phases in the disease progression. Several authors propos...

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale exper...

This paper addressed the problem of Out-Of-Vocabulary (OOV) utterance detection in small vocabulary telephone keyword spotting system. We propose a new approach for modeling OOV words in the scenario of a small vocabulary of telephone keyword spotting system. The paper adopt the semi-continuous Hidden Markov Model with multiple codebooks to modeling the keywords. We propose a two pass procedure...

Rating how well a routine activity is performed can be valuable in a variety of domains. Making the rating inexpensive and credible is a key aspect of the problem. We formalize the problem as MAP estimation in HMMs where the incoming trace needs repair. We present polynomial time algorithms for computing minimal repairs with maximal likelihood for HMMs, Hidden Semi-Markov Models (HSMMs) and a f...