We investigate two statistical detection problems in plasma etch endpoint detection: change-point detection and pattern matching. Our approach is based on a segmental semi-Markov model framework. In the change-point detection problem, the change-point corresponds to state switching in the model. For pattern matching, the pattern is approximated as a sequence of linear segments that are modeled ...

Although the segmental intelligibility of converted speech from silent speech using direct signal-to-signal mapping proposed by Toda et al. [1] is quite acceptable, listeners have sometimes difficulty in chunking the speech continuum into meaningful words due to incomplete phonetic cues provided by output signals. This paper studies another approach consisting in combining HMM-based statistical...

Speech can be represented as a time/frequency distribution of energy using a multi-band filter bank. A Markov random field model, which takes into account the possible time asynchrony across the bands, is estimated for each segmental units to be recognized. The law of the speech process is given by a parametric Gibbs distribution and a maximum likelihood parameter estimation algorithm is develo...

This paper presents several improvements to our voice conversion system which we refer to as Speaker Transformation Algorithm using Segmental Codebooks (STASC)[2]. First, a new concept, sentence HMM, is introduced for the alignment of speech waveforms sharing the same text. This alignment technique allows reliable and high resolution mapping between two speech waveforms. In addition, it is obse...

Human and other primate genomes consist of many segmental duplications (SDs) due to fixation of copy number variations (CNVs). Structure of these duplications within the human genome has been shown to be a complex mosaic composed of juxtaposed subunits (called duplicons). These duplicons are difficult to be uncovered from the mosaic repeat structure. In addition, the distribution and evolution ...

We propose a stochastic pronunciation model using an ergodic hidden Markov model (EHMM) of automatically derived acoustic sub-word units (SWU). The proposed EHMM discovers the pronunciation structure inherent in the acoustic training data of a word without any apriori phonetic transcriptions. The EHMM is an HMM of HMMs – its states are SWU HMMs and the state-transitions compose various possible...

This work presents training methods and recogni tion experiments for phoneme wise tied mixture den sities in hidden Markov models HMM The system trains speaker dependent but vocabulary independent phoneme models for the recognition of Finnish words The Learning Vector Quantization LVQ methods are applied to increase the discrimination between the phoneme models A segmental LVQ training is pro p...

This paper presents methods to improve the probability density estimation in hidden Markov models for phoneme recognition by exploiting the Self-Organizing Map (SOM) algorithm. The advantage of using the SOM is based on the created approximative topology between the mixture densities by training the Gaussian mean vectors used as the kernel centers by the SOM algorithm. The topology makes the ne...

This article presents a cross-lingual study for agglutinative, fixed stressed languages, like Hungarian and Finnish, about the segmentation of continuous speech on word level by examination of supra-segmental parameters. We have developed different algorithms based either on a rule based or a datadriven approach. The best results were obtained by data-driven algorithms (HMMbased methods) using ...

Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in some instances matching two segments of points may be preferred and can result in increased noise robustness. This paper presents an approach to segmental sequence alignment based on adaptive pairwise segmentation. We introduce a distance metric be...