In this paper we present a novel method to incorporate temporal correlation into a speech recognition system based on conventional hidden Markov model (HMM). In our new model the probability of the current observation not only depends on the current state but also depends on the previous state and the previous observation. The joint conditional PD is approximated by non-linear estimation method...

automatic segmentation of multiple sclerosis (ms) lesions in brain magnetic resonance imaging (mri) has been widely investigated in the recent years with the goal of helping ms diagnosis and patient follow‑up. in this research work, gaussian mixture model (gmm) has been used to segment the ms lesions in mris, including t1‑weighted (t1‑w), t2‑w, and t2‑fluid attenuation inversion recovery. usual...

Random variables and probabilistic decision making are important elements in most theories of reading eye movements, but they tend to receive little theoretical attention. This paper attempts to address this problem by introducing the Stochastic, Hierarchical Architecture for Reading Eye-movements (SHARE). The SHARE framework formalizes reading eye movements as observable outcomes of a latent s...

Density estimation with Gaussian Mixture Models is a popular generative technique used also for clustering. We develop a framework to incorporate side information in the form of equivalence constraints into the model estimation procedure. Equivalence constraints are defined on pairs of data points, indicating whether the points arise from the same source (positive constraints) or from different...

Vibration signals from complex rotating machines are often non-Gaussian and non-stationary, so it is difficult to accurately detect faults of a bearing inside using a single sensor. This paper introduces a new bearing fault diagnostics scheme in complex rotating machines using multi-sensor mixtured hidden Markov model (MSMHMM) of vibration signals. Vibration signals of each sensor will be consi...

Hidden Markov Model (HMM) is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively...

We propose a layered dynamic mixture model for asynchronous multi-modal fusion for unsupervised pattern discovery in video. The lower layer of the model uses generative temporal structures such as a hierarchical hidden Markov model to convert the audiovisual streams into mid-level labels, it also models the correlations in text with probabilistic latent semantic analysis. The upper layer fuses ...

In much of the literature devoted to face recognition, experiments are performed with controlled images (e.g. manual face localization, controlled lighting, background and pose); however, a practical recognition system has to be robust to more challenging conditions. In this paper we first evaluate, on the relatively difficult BANCA database, the performance, robustness and complexity of Gaussi...

This paper describes how formant frequencies of voiced and unvoiced speech can be predicted from mel-frequency cepstral coefficients (MFCC) vectors using maximum a posteriori (MAP) estimation within a hidden Markov model (HMM) framework. Gaussian mixture models (GMMs) are used to model the local joint density of MFCCs and formant frequencies. More localised prediction is achieved by modelling s...

This thesis presents a novel approach to speech enhancement by exploiting the bimodality of speech production and the correlation that exists between audio and visual speech information. An analysis into the correlation of a range of audio and visual features reveals significant correlation to exist between visual speech features and audio filterbank features. The amount of correlation was also...