e acoustic-phonetic approach, the speech is not segmented nor checked for its properties. If enough patterns are inputted to the speech recognition system during training, it will perform better than the acoustic-phonetic approach. In general, statistical pattern recognition approach is used more than acoustic-phonetic approach because it is simpler to use, invariant to different speech vocabularies, and more accurate (higher performance)(7, p.44).Modern Approach to Automatic Speech RecognitionWith the availability of computers and high speed microprocessors, more research was done using the huge computational power available to solve the speech recognition problem. However, scientists, till now, don’t know the solution. Nevertheless, they were able to implement new approaches that proved to be much more efficient than earlier methods. Speech recognition systems are able to recognise more words and with more accuracy (3, p.115). Some of these approaches are presented below. Hidden Markov Models (HMMs)Speech is divided into phonemes. Unfortunately, these phonemes do not remain the same, they change according to the surrounding phonemes (4, p.44). HMMs are a tool to represent these changes mathematically.A Markov model consists of a number of states linked together with each state corresponding to a unique output. Each link between two states is characterised by a probability called transitional probability (4, p.44). Moving from one state to another or remaining in the same state is function of the corresponding transitional probability (2, p.50). A classical example illustrating Markov models is the following: consider a three-state weather system with state one being rainy, state two cloudy, and state three sunny. Such a system is shown in figure 2 (transitional probabilities are added for explanation below). From the diagram, it is clear that if the current day is sunny, the probability of tomorrow being clou...
