riate word sequencing. However, grammar doesn’t allow random dictation which is a problem for some applications (6, p.98).Continuous vs. Discrete Speech When speaking to each other, we don’t pause between words. In other words, we use continuous speech. However, for speech recognition systems, there is difficulty in dealing with continuous speech (6, p.98). The easy way out will be using discrete speech where we pause between words (6, p.100). With discrete speech input, the silent gap between words is used to determine the boundary of the word, whereas in continuous speech, the speech recognition system must separate words using an algorithm which is not a hundred per cent accurate. Still, for a small vocabulary and using grammar, continuous speech recognition systems are available. They are reliable and do not require great computational power (6, p.100). However, for large vocabulary, continuous speech recognition systems are very difficult to achieve, require huge computational power, as well as being slow. In fact, processing a speech sample can take three to ten times the time required for a person to say it (6, p.100).Speaker DependencySpeech recognition system designers must consider another important issue: whether their systems are speaker-dependent or speaker-independent. Each person pronounces a word differently. Although it is easy for humans to recognise the word ‘car’ whether an American or an Englishman says it, for speech recognition systems, this is not the case. Speaker dependency is determined from the application, some may require speaker-dependent systems (as in data entry), others may require speaker-independent systems (as in automated call-type recognition)(6, p.100). Speaker dependency affects greatly the training of an automatic speech recognition system (4, p.42). Early Approaches to Automatic Speech RecognitionWhen scientists dreamed about a machine capable o...
