Hidden Markov Model is an important approach applied to activity recognition. In the first-order Hidden Markov Model, there is the hypothesis that the transition probability of state and the output probability of observation are only dependent on the current state of the model, which debases the precision of information extraction comparatively. In second-order Hidden Markov Model, the relevanc...

Today’s state-of-the-art speech recognition systems typically use continuous density hidden Markov models with mixture of Gaussian distributions. Such speech recognition systems have problems; they require too much memory to run, and are too slow for large vocabulary applications. Two approaches are proposed for the design of compact acoustic models, namely, subspace distribution clustering hid...

Figure 1. Finite state machine for a Markov chain X0 → X1 → X2 → · · · → Xn where the random variables Xi’s take values from I = {S1, S2, S3}. The numbers T (i, j)’s on the arrows are the transition probabilities such that Tij = P (Xt+1 = Sj|Xt = Si). Definition 1.2. We say that (Xn)n≥0 is a Markov chain with initial distribution λ and transition matrix T if (i) X0 has distribution λ; (ii) for ...

Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov ...