All of these notions are captured by a model known as the Hidden Markov Model. A HMM is basically a finite automata in which each transition has a probability associated with it. A given vocabulary word has a HMM which is designed to model the many possible strings of phones which may be produced by the utterance of the word. Each expected phone is generally represented by a state in the HMM, while each possible phone at every stage has an arc. This it that a 'y' may be represented as a 'y' or an 'i' arc, while both lead to the 'y' state.
Self loops account for the possibility of a phone stretching over several blocks.
Missed phones are also allowed, as an arc may jump over a state. Then on an input signal, a dynamic programming algorithm, called the Viterbi algorithm, is applied to identify which HMM is the most likely match for the input signal. The task of speech recognition has been seen to be one involving many techniques. Some modern improvements on older processes have shown to be of great help in such tasks as formant identification, word separation, and phone separation. Accurate speech recognition is possible, as this project has demonsrated, but as the size of the vocabulary grows, so do the number of techniques required to differentiate between words.