This paper deals with the automatic segmentation for Czech Concatenative speech synthesis. Statistical approach to speech segmentation using hidden Markov models (HMMs) is applied in the baseline system [1]. Several experiments that concern various issues in the process of building the segmentation system, such as speech parameterization or HMM initialization problems, are described here. An ob...

texture image analysis is one of the most important working realms of imageprocessing in medical sciences and industry. up to present, different approacheshave been proposed for segmentation of texture images. in this paper, we offeredunsupervised texture image segmentation based on markov random field (mrf)model. first, we used gabor filter with different parameters’ (frequency,orientation) va...

This paper encompasses the approaches of segmental modelling and the use of dynamic features in addressing the constraints of the IID assumption in standard HMM. Phonetic features are introduced which capture the transitional dynamics across a phoneme unit via a DCT transformation of a variable length segment. Alongside this, the use of a hybrid phoneme model is proposed. Classification experim...

This paper describes an unsupervised dynamic graphical model for morphological segmentation and bilingual morpheme alignment for statistical machine translation. The model extends Hidden Semi-Markov chain models by using factored output nodes and special structures for its conditional probability distributions. It relies on morpho-syntactic and lexical source-side information (part-of-speech, m...

A common approach to analysis of mouse behavior recorder by video tracking systems employs manual segmentation and labeling of mouse activity into behavioral acts. Developed automatic methods allow segmentation only to lingering and progression segments, suffer from poor precision and require parameter tuning. We propose a novel approach based on hidden Markov model for simultaneous segmentatio...

In any problem involving images having scale-dependent structures, a key issue is the modeling of these multi-scale characteristics. Because multi-scale phenomena frequently possess nonstationary, piece-wise multi-model behaviour, the classic hidden Markov method can not perform well in modeling such complex images. In this paper we provide a new modeling approach to extend previous hierarchica...

We study the problem of topic segmentation of manually transcribed speech in order to facilitate information extraction from dialogs. Our approach is based on a combination of multi-source knowledge modeled by hidden Markov models. We experiment with different combinations of linguistic-level cues on dialogs dealing with search and rescue missions. Results show the effectiveness of multi-source...

We present a Chinese word segmentation system which ran on the closed track of the simplified Chinese Word Segmentation task of CIPS-SIGHAN-CLP 2010 bakeoffs. Our segmenter was built using a HMM. To fulfill the cross-domain segmentation task, we use semi-supervised machine learning method to get the HMM model. Finally we get the mean result of four domains: P=0.719, R=0.72

Machine learning techniques have been applied to several kinds of human data including speech recognition and goal or user identification. When learning on such data, it is important to use models that are not strongly biased against properties of the data, or the variable assignments learned may be largely incorrect. We are working with data sources for user interface event data and examining ...

This paper presents a novel hybrid modeling technique that is used for the first time in Hidden Markov Modelbased handwriting recognition. This new approach combines the advantages of discrete and continuous Markov models and it is shown that this is especially suitable for modeling the features typically used in handwriting recognition. The performance of this hybrid technique is demonstrated ...