are also affected by surrounding syllables, but their number is much greater than phones (around 20 000 in English) which make them hard to train (1, p.53). A new sub-word unit, known as triphone, seem to be the most successful. Triphones solve the problem of influence between sub-word units and their surrounding by modelling each phone according to its right and left neighbour (1, p.53). As an example, the ‘t’ in ‘still’ will be modelled by the s-t-i triphone (1, p.53). The immediate problem one can think of is the large number of triphones since we are taking each phone and combining it with all possible left and right phone neighbours. This problem can be resolved by using the fact that some triphones can be very similar since many neighbouring phones can affect a phone the same way (1, pp.53-54). For example, the effect on the ‘t’ in ‘still’ is similar to the one in ‘steal’ (1, pp.53-54). Even though the performance of the recognition system is affected by such approximations, it remains within acceptable standards (1, p.54). Applications of Automatic Speech Recognition SystemsWith all the time and money spend on researches on speech recognition systems, someone may wonder about the applications of speech recognition. This part will present some of the currently available applications along with some future applications of automatic speech recognition systems.Automated Call-Type RecognitionAn interesting and relatively simple application of speech recognition systems is automated call-type recognition. In pay phones, operators are needed to determine the call-type of the caller (7, p.490). Speech recognition may be used instead of operators. Five types of calls are available: ‘collect’, ‘calling card’, ‘operator’ for operator assisted calls, ‘third number’ for third party billing calls, ‘person&#...
