This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed for model selection on independent hidden variables (i.e., mixture models), for time-dependent hidden variables. As with FAB in mixture models, FAB for HMMs is derived as an iterative lower bound maximization algorithm...

Thanks to Automatic Speech Recognition (ASR), a lot of machines can nowadays emulate human being ability to understand and speak natural language. However, ASR problematic could be as interesting as it is difficult. Its difficulty is precisely due to the complexity of speech processing, which takes into consideration many aspects: acoustic, phonetic, syntactic, etc. Thus, the most commonly used...

Hidden Markov Models are widely used in speech recognition and bioinformatics systems. Conventional methods are usually used in the parameter estimation process of Hidden Markov Models (HMM). These methods are based on iterative procedure, like BaumWelch method, or gradient based methods. However, these methods can yield to local optimum parameter values. In this work, we use artificial techniq...

We present a new model, derived from classical Hidden Markov Models (HMMs), to learn sequences of large Boolean vectors. Our model – Hidden Markov Model with Patterns, or HMMP – differs from HMM by the fact that it uses patterns to define the emission probability distributions attached to the states. We also present an efficient state merging algorithm to learn this model from training vector s...

The previous lecture introduced hidden Markov models (HMMs), a technique used to model types of sequences rather than exact matches. Recall that a Markov chain consists of states Q, initial state probabilities p, and transition state probabilities A. A hidden Markov model has the additional property of emitting a series of observable outputs, one from each state, with various emission probabili...

Fingerprint identification system is mainly consisted of fingerprint achieving, fingerprint classification and fingerprint matching. Fingerprint matching is the key to the system and effects on the precision and efficiency of the whole system directly. Fingerprints are matched mainly based on their fingerprint texture pattern which can be described with the orientation field of fingerprints. A ...

Bayesian models provide powerful tools for analyzing complex time series data, but performing inference with large datasets is a challenge. Stochastic variational inference (SVI) provides a new framework for approximating model posteriors with only a small number of passes through the data, enabling such models to be fit at scale. However, its application to time series models has not been stud...

The estimation of an unobservable process x from an observed process y is often performed in the framework of Hidden Markov Models (HMM). In the linear Gaussian case, the classical recursive solution is given by the Kalman filter. On the other hand, particle filters provide approximate solutions in more complex situations. In this paper, we propose two successive generalizations of the classica...

Transient scattered fields from a general target are composed of wavefronts, resonances and time delays, with these constituents linked to the target geometry. A classifier applied to transient scattering data requires a statistical model for such fundamental constituents. A Markov model is employed to characterize the transient scattered fields for a set of target-sensor orientation over which...

The wireless power terminals are deployed in harsh public places and lack strict control, facing security problems. Thus, they faced with problems such as illegal counterfeit terminal access, unlawful control of connected terminals, etc. intrusion detection system based on machine learning artificial intelligence significantly improve the side’s abnormal capacity. In this article, we aim at ide...