being in a certain state relies on the previous state (4, p.44).Training of an Automatic Speech Recognition System Based on HMMsAs mentioned earlier, a major component of an HMM system are the probabilities between states and the probability distribution of each state. To have a good speech recognition system, these probabilities must change to factors like language, possible number of speakers, and so on (3, p.115). Determining these probabilities is part of what is known as training the speech recognition system.This training process depends on whether we are dealing with a speaker-dependent or a speaker-independent speech recognition system. In the first case, speech samples are taken from the user and the probabilities are determined accordingly. In the second case, speech samples are accumulated from many speakers in addition to the text of what was said. In this case, the training process is much more complicated since the spectrogram (measure of frequency vs. time) of the same word depends on the speaker. A training process consists also of implementing a dictionary holding the vocabulary along with a grammar of permitted word sequences (4, p.42). Sub-Word UnitsIn HMMs, each word is represented by a sequence of states (1, p.53). A word is recognised from the sequence of states that is most probably associated with a sequence of outputs. Therefore, the unit for such HMMs is the word. Many scientists believe that using sub-words instead of words may improve the quality of speech recognition (1, p.50). To implement sub-word HMMs, a system of sub-word units must by selected. The simplest form of sub-word units are phones. Using phones as units for an HMM seems to be the right choice since phones are small in number and smoothly trained, but the performance of such an HMM is poor since a phone is affected by the surrounding phones (1, p.53). Another choice of sub-word units are syllables. Similar to phones, syllables...
