Introduction: Evoked potentials arisen by stimulating the brain can be utilized as a communication tool  between humans and machines. Most brain-computer interface (BCI) systems use the P300 component,  which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for  detection of P300.  Materials and Methods: The wavelet transforms, wavelet-enhanced indepen...

It has now been over 35 years since hidden Markov Models were first applied to the problem of speech recognition ([2], [7]). Moreover, it has now been over 20 years since the speech recognition community seemed to collectively adopt the HMM paradigm as the most useful general approach to the fundamental problem of modeling speech. Perhaps a key turning point in this regard was Kai-Fu Lee’s thes...

This paper presents a set of novel duration features for detecting pitch accent and phrase boundaries, which depend on articulatory timing rather than segmental duration information. The features are computed from the detected syllable nuclei and boundaries, using peaks and valleys in an energy contour but also leveraging information from a simple HMM phone manner class recognizer to increase r...

BACKGROUND The second heart rate (HR) turn point has been extensively studied, however there are few studies determining the first HR turn point. Also, the use of mathematical and statistical models for determining changes in dynamic characteristics of physiological variables during an incremental cardiopulmonary test has been suggested. OBJECTIVES To determine the first turn point by analysi...

Discriminative segmental models, such as segmental conditional random fields (SCRFs), have been successfully applied to speech recognition recently in lattice rescoring to integrate detectors across different levels of units, such as phones and words. However, the lattice generation has been constrained by a baseline decoder, typically a frame-based hybrid HMMDNN system, which still suffers fro...

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long divided into several sections and the occurs in different section that defined as state of (HMM). The hybrid HMM, i.e., DNN-HMM, consists (DNN) with multiple layers exploit non-linear data. DNN initialized by using belief (DBN). DBN pre-trained built stacking top-down restr...

Hidden Markov models (HMMs) have been widely used to model speech signals for speech recognition. However, they cannot precisely model the time dependency of feature parameters. In order to overcome this limitation, several researchers have proposed extensions, such as segmental unit input HMM (Nakagawa & Yamamoto, 1996). Segmental unit input HMM has been widely used for its effectiveness and t...

The intelligibility of HMM-based TTS can reach that of the original speech. However, HMM-based TTS is far from natural. On the contrary, unit selection TTS is the most-natural sounding TTS currently. However, its intelligibility and naturalness on segmental duration and timing are not stable. Additionally, unit selection needs to store a huge amount of data for concatenation. Recently, hybrid a...

This paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. The Segmental K-Means algorithm is used for updating the transition and observation probabilities, instead of the Baum-Welch algorithm. Observation probabilities are modelled as multi-variate Gaussian mixture distributions. A deterministic clustering technique is used to estimate the initial para...