A hidden Markov model (HMM) comprises a state with Markovian dynamics that can only be observed via noisy sensors. This paper considers three problems connected to HMMs, namely, inverse filtering, belief estimation from actions, and privacy enforcement in such context. First, the authors discuss how HMM parameters sensor measurements reconstructed posterior distributions of an filter. Next, con...

Speaker verification from talking a few words of sentences has many applications. Many methods as DTW, HMM, VQ and MQ can be used for speaker verification. We applied MQ for its precise, reliable and robust performance with computational simplicity. We also used pitch frequency and log gain contour for further improvement of the system performance.

We present a generative factor analyzed hidden Markov model (GFA-HMM) for automatic speech recognition. In a standard HMM, observation vectors are represented by mixture of Gaussians (MoG) that are dependent on discretevalued hidden state sequence. The GFA-HMM introduces a hierarchy of continuous-valued latent representation of observation vectors, where latent vectors in one level are acoustic...

Using hidden Markov models (HMM), the current study looked at how learners' metacognitive monitoring is related to their physiological reactivity in context of collaborative learning. The participants (N = 12, age 16–17 years, three females and nine males) were high school students enrolled an advanced physics course. results show that during learning, engaged each self-regulated learning phase...

The adaptive market hypothesis (AMH) has recently attracted significant interest in the financial literature. AMH started to be considered an alternative efficient hypothesis. In this respect, study, first of all, examines for BIST100 index Turkey’s Borsa Istanbul stock exchange by testing return predictability. applications are performed via automatic portmanteau and generalized spectral (GS) ...

In this paper, we propose an algorithm for estimating the parameters of a time-homogeneous hidden Markov model (HMM) from aggregate observations. This problem arises when only population level counts number individuals at each time step are available, and one seeks to learn individual HMM these Our is built upon classical expectation–maximization recently proposed inference (Sinkhorn belief pro...