Hidden Markov models . labelling

The first step of the analysis is the training where Hidden Markov Models (HMMs) representing various operating behaviors are trained using labeled historical data from the process. In this section, three broad operat-... 

Finally, using a linear phonemic representation has the benefit that it makes automatic database labelling considerably easier. In Chapter 17 we shall consider the issue of labelling a speech database with phoneme units, both by hand and by computer. As we shall see, it is easier to perform this labelling if we assume a linear pronunciation model, as this works well with automatic techniques such as hidden Markov models. 

While many possible approaches to statistical synthesis are possible, most work has focused on using hidden Markov models (HMMs). This along with the unit selection techniques of the next chapter are termed third generation techniques. This chapter gives a full introduction to these and explains how they can be used in synthesis. In addition we also show how these can be used to automatically label speech databases, which finds use in many areas of speech technology, including unit selection synthesis. Finally, we introduce some other statistical synthesis techniques. 

One can obtain the labels and offsets of every term from the text. There are various statistical models that can be used in this process. Hidden Markov model (HMM) is the simplest of dynamic Bayesian model. HMM is a finite set of states, each of which is associated with a (generally mirltidimensional) probability distribution [41]. HMMs ate a form of generative models that define a joint probabihty... 

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