Context-sensitive modelling

We use context sensitive models in ASR as they have lower variance than the general phone models. In synthesis, we use context sensitive models for a similar reason, so that a model in a particular context generates observations appropriate to that context only. In synthesis however, we are interested in a broader range of factors, as we have to generate the prosodic variation as well as simply the verbal, phonetic variation. 

The context-sensitive models are built in exactly the same way as described in Section 15.1.9, and the resultant decision tree provides a unique mapping from every possible feature combination to HMM model parameters. One significant point of note is that in synthesis the extra prosodic features mean that number of possible unique feature combinations can be many orders of magnitude larger than in ASR. Given that we may be training on less data than in ASR, we see that the sparse data problems can be considerably worse. We will return to this issue in Section 16.4. 

The use of compartmental models leads onto the subject of context-sensitive half time (CSHT). 

Hughes MA, Glass PS, Jacobs JR. Context-sensitive half-time in multicompartment pharmacokinetic models for intravenous anesthetic drugs. Anesthesiology 1992 76 334—41. 

The abstract syntax of collaboration diagrams is defined by the graph schema shown in Fig. 5.67, which will be explained later. A formal definition of the abstract syntax, again, enables the modeling environment to guarantee (syntactical) correctness and completeness with respect to context-free as well as context-sensitive conditions, such as tjrpe conformity of actual and formal parameters to facilitate the generation of executable code. 

Samudrala and Moult described a method for handling context sensitivity of protein structure prediction, that is, simultaneous loop and side-chain modeling, using a graph theory method [198, 209] and an all-atom distance-dependent statistical potential energy function [199]. Their program RAMP is listed in Table 5.6. 

Ease of use and intuitiveness The software should have features to help automate the modelbuilding process. Desirable features might include menu-driven operation, fill-in menus, and prompting for omitted values. The casual user requires online, context-sensitive help in the model-building process. The software should be accessible in an interactive mode. 

With all this background and theoretical connections, it is easy to understand how NEPs can be described as agential bio-inspired context-sensitive systems. Many disciplines are needed of these types of models that are able to support a biological framework in a collaborative environment. The conjunction of these features allows applying the system to a number of areas, beyond generation and recognition in formal language theory. 

Keywords Finite automata Mildly context-sensitive languages Natural languages Formal linguistics Free-order languages Computational linguistics Formal models... 

In the previous section we gave some examples how NFAwtls/DFAwtls can be used to model languages that are not context-free and closely connected to the main mildly context-sensitive languages. In this section we make a further step we use NFAwtls to present some features of a natural language, namely, of the Hungarian language. 

We first show by a counterexample that context free languages are insufficient model for organic synthesis. We then argue (alas we cannot prove, for the above reasons) that context sensitive languages are a sufficient model. 

Once the production resources have been selected, it is necessary to select the appropriate values for controllable parameters in manufacturing operations, hi product manufacturing scenarios, parameters such as cutting speed, feed, and depth of cut or width of cut need to be selected for each feature. In provision of services, parameters are more context sensitive. For example, in providing a helpdesk to technically support a software system, the number of persormel is a parameter that needs to be chosen. Various models are used for selection of operational parameters including the minimum cost models that seek to minimize the overall resource cost of the operation, maximum production rate models that aim to realize the highest possible throughput and lead time- oriented models that strive to lower the production s time to market. 

Yoon, B.-J. Vaidyanathan, P.P. (2005). Optimal alignment algorithm for context-sensitive hidden Markov models. Proceedings of the 30th International Conference on Acoustics, Speech, and Signal Processing, Philadelphia. 

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