Language models

In Equation (15.8) the term P W) gives us the prior probabihty that the sequence of words W = (wi, u)2. wn) will occur. Unlike in the case of acoustic observations, we know of no natural distribution that models this. Partly this is due to the fact that the number of possible sentences (i.e. unique combinations of words) is extremely large. We therefore model sentence probabilities by a counting technique as follows. 

The chain rule in probability (see Appendix A) can be used to break this into a number of terms  

The estimation of this becomes tractable if we shorten the number of words such that we approximate the term P(Wj it i,..as 

That is, we estimate the probability of seeing word Wi on a fixed-window history of the N previous words. This is known as an Ai-gram language model. Its basic probabilities are estimated from counting occurrences of the sequences in a corpus. 

Given the input observation sequence 0= oi,02.or and a word sequence W = wi,W2, , we can use Equation 15.7 to find the probability that this word sequence generated those observations. The goal of the recogniser is to examine all possible word sequences and find the one W which has the highest probability according to our model  

HMMs are generators, configured so that we have a model for each linguistic unit which generates sequences of frames. Hence the HMM for a single word w, which has been concatenated from single phone models, is of the form 

So the HMM itself gives us the probability of observing a sequence of frames given the word, and not the probability we require which is that of observing the word given the frames. We convert one to the other using Bayes rule (SectionA.2.8) so that  

P(W) is called the prior and represents the probability that this particular sequence of words occius independently of the data (after all, some words are simply more common tiian others). P 0 W) is called the likelihood and is die probability given by our HMMs. P 0) is the evidence and is the probability that this sequence of frames will be observed independent of everylliing else. As this is fixed for each utterance we are recognising, it is common to ignore this term. 

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Java presents a compelling technical base for component-based development, including enterprise-scale server components. Its main drawback is the single-language model, which is addressed by the integration of Java and CORB A. 

On November 11, 2003, Ray Kurzweil and John Keklak received U.S. Patent 6,647,395 for software that creates poetry. These programs read a selection of poems and then create a language model that allows the program to write original poems from that model. This means that the system can emulate words and rhythms of human poets to create new masterpieces. The system can also be used to motivate human authors who have writer s block and are looking for help with alliteration and rhyming. 

Applications in a Natural Language Modeling Some Structures of Hungarian Language... 

Text as language models In this model, the process is seen as basically one of synthesis alone. The text itself is taken as the linguistic message, and synthesis is performed from this. As the text is rarely clean or unambiguous enough for this to happen directly, a text normalisation process is normally added, as a sort of pre-processor to the synthesis process itself. The idea hear is that the text requires tidying-up before it can be used as the input to the synthesiser. 

To use these equations for recognition, we need to connect state sequences with what we wish eventually to find, that is, word sequences. We do this by using the lexicon, so that, if the word hello has a lexicon pronunciation /h eh 1 ou/, then a model for the whole word is created by simply concatenating the individual HMMs for the phones /h/, /eh/, /y and /ou/. Since the phone model is made of states, a sequence of concatenated phone models simply generates a new word model with more states there is no qualitative difference between the two. We can then also join words by concatenation the result of this is a sentence model, which again is simply made from a sequence of states. Hence the Markov properties of the states and the language model (explained below) provide a nice way of moving from states to sentences. 

Vhdl was approved as an IEEE standard in 1987 and has gained considerable momentum in the last few years [18, 1]. The language model can be described as a network of interconnected components, each of which has an algorithmically described behavior. The expressive power of the language is very large all basic data types, including subranges, records, and arrays, are supported ... 

Based on this language model-based FTA, automation has been developed. OSATE tool may be used to generate fault tree. The generation tool is designed to be flexible and can be re-targeted to more than one fault tree analysis tool. The portion of the tool that extracts the system instance error model can be reused to generate different types of safety artifacts, such as Markov Chains. 

M. Mahmood, F. Mavaddat, M.I. Elmasiy, and M.H.M. Cheng, A Formal Language Model of Local Microcode Synthesis , Proc. of the IMEC-IFIP Workshop on Applied Formal Methods for Correct VLSI Design, pages 21-39, November, 1989. 

In this paper we discuss relationships between languages, models and tools for synthesis-driven design-methodology. We will also discuss essential issues derived from those relationships and some possible solutions. We will also paint with a broad brush, an ideal system for high-level synthesis and propose solutions for some essential issues. Finally, we will discuss future research trends driven by this evolutionary extension of synthesis to higher abstraction levels. 

Keywords AADL LNT Distributed real-time systems - Architecture description languages Model transformation Specification languages -Formal verification... 

A language model, which is statistical information associated with a vocabulary that describes the likelihood of the occurrence of words and sequences of words in the user s speech... 

The software must be customized to the voice of the speaker. This is the training part, which consists of the user s reading a passage from a prepared text. The program then adds the data to the information it already knows about the sounds in the language. When training a user, it starts with a standard set of models and then customizes them for the way the user speaks (acoustic model) and the way he or she uses words (vocabulary and associated language model). 

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