Our work deals with the classical problem of merging heterogenous and asynchronous parameters. It's well known that lips reading improves the speech recognition score, specially in noise condition ; so we study more precisely the modeling of acoustic and labial parameters to propose two Automatic Speech Recognition Systems : a Direct Identi cation is performed by using a classical HMM approach ...

This paper presents a novel approach to robust estimation of linear prediction (LP) model parameters in the application of speech enhancement. The robustness stems from the use of prior knowledge on the clean speech and the interfering noise, which are represented by two separate codebooks of LP model parameters. We propose to model the temporal dependency between short-time model parameters wi...

In our previous research, we demonstrated the validity of segmental unit input hidden Markov model (HMM), which regards successive four frame MEL-cepstrum coefficients as a feature vector. The vector is reduced to lower dimensions using the KL transform. However, the model considers only the correlation between frames in a short section, but not the correlation between the frames over a long se...

This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...

We formulate the problem of change-point detection in a segmental semi-Markov model framework where a change-point corresponds to state switching. The semi-Markov part of the model allows us to incorporate prior knowledge about the time of change in a Bayesian manner. The segmental part of the model allows exible modeling of the data within individual segments, e.g., as linear, quadratic, or ot...

This paper proposes a general probabilistic framework for shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize waveform shape and shape variation is captured by adding random effects to the segmental model. The resulting probabilistic framework provides a basis for learning of waveform models from data as well as parsing and rec...

In an effort to advance the state of the art in continuous speech recognition employing hidden Markov models (HMM), Segmental Neural Nets (SNN) were introduced recently to ameliorate the wellknown limitations of HMMs, namely, the conditional-independence limitation and the relative difficulty with which HMMs can handle segmental features. We describe a hybrid SNN/I-IMM system that combines the ...

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...