We consider the log-likelihood function of hidden Markov models, its derivatives and expectations of these (such as different information functions). We give explicit expressions for these functions and bound them as the size of the chain increases. We apply our bounds to obtain partial second order asymptotics and some qualitative properties of a special model as well as to extend some results...

Hidden Markov models are widely used in the areas of speech recognition and bioinformatics. Hidden Markov models differ from simple Markov models by including hidden states in addition to observable states. For example in bioinformatics, it is not easy to figure out what lies beneath the sequences by using simple Markov models. Once the Hidden Markov Model structure is determined, there are thr...

We present a method for summarizing speech documents without using any type of transcript/text in a Hidden Markov Model framework. The hidden variables or states in the model represent whether a sentence is to be included in a summary or not, and the acoustic/prosodic features are the observation vectors. The model predicts the optimal sequence of segments that best summarize the document. We e...

Drivers’ individual difference is one of the key factors to influence the accuracy of driving behavior model. The accuracy of model should include the effect characteristics of individual difference on driving behavior. The overtaking process was the research object to study the individual characteristics of driving behavior. The operation data of accelerator and steering wheel of each driver w...

In this paper, we present an application of the hierarchical HMM for structure discovery in educational videos. The HHMM has recently been extended to accommodate the concept of shared structure, ie: a state might multiply inherit from more than one parents. Utilising the expressiveness of this model, we concentrate on a specific class of video – educational videos – in which the hierarchy of s...

This paper describes a technique of estimating style expressiveness for an arbitrary speaker’s emotional speech. In the proposed technique, the style expressiveness, representing how much the emotions and/or speaking styles affect the acoustic features, is estimated based on multiple-regression hidden semiMarkov model (MRHSMM). In the model training, we first train average voice model using mul...

This paper examines audio-visual speaker veri cation using a novel adaptation of fused hidden Markov models, in comparison to output fusion of individual classi ers in the audio and video modalities. A comparison of both hidden Markov model (HMM) and Gaussian mixture model (GMM) classi ers in both modalities under output fusion shows that the choice of audio classi er is more important than vid...

In this paper we will describe an implementation of a score-performance matching, capable of score following, based on a stochastic approach using Hidden Markov Models.