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Zadinksy et al in their paper entitled Speech Technologies And Its Impact on Medical Decision, presented at the 2002 Annual HIMSS Conference, defined speech recognition as "The conversion of spoken words into computer text. Speech is digitised and matched against a dictionary of coded waveforms. The matches are converted into text as if the words are typed on the keyboard."

The first quest into the development of speech recognition was carried out in the 1940’s by the US Department of Defense. In the 1950’s, research was funded by the Defense Advanced Research Project Agency (DARPA) and in 1952; Bell Laboratories developed an automatic speech recognition system that could successfully identify the digits from 0 to 9 spoken to it over the telephone.

Subsequent improvements were few and far in-between, mainly restricted to laboratories and required serious computing power. The introduction of continuous speech recognition (CSR) and high-speed processor chips saw the technology move on to the Personal Computer (PC).


There are currently two types of speech recognition; discrete and continuous. Discrete speech recognition processes speech word by word and has a slower dictation process. Early speech recognition was based on this model. Continuous speech recognition processes speech by phrases and takes context into account. It is faster but lass accurate if the phrases are broken. Modern speech technology applications are based on this model.

The "moderate success" of continuous speech recognition applications has been restricted mainly to clinical domains where they have been used in scenarios where dictation is required - autopsy and radiology reports, and operating rooms. They also used in the area of medical transcriptions. Electronic medical records (EMRs) vendors such as McKesson have added the technology as an optional feature on their products.

Many have tried to champion CSR as a viable technology but have been firmly and probably rightly met by some opposition. Opposition have argued that "out of box" applications have an error rate of 5 – 10%and at best improve to a 3% error rate with training of the computer, where it learns to recognise the user’s voice. Documents have to created have to corrected for errors during creation or afterwards. Some argue that the correction process can sometimes take as long as actually typing out the whole document using a keyboard.

Also it has been noted that to ensure the quality of the recordings that ambient noise must be kept to a minimum and this usually means finding a quiet room with little interruptions (which is not always practicable) and the use of high quality or special headsets and microphones.

Commercial software organisations involved in the production of CSR applications have gone through some changes in the past few years. In 1996, they were four major players in the market – Dragon Systems, IBM, Lernout and Heuspie (L&H) and Philips. However in the year 200, Dragon Systems was bought over by L&H, who 18 months later declared bankruptcy and was bought by ScanSoft This might be indication of how difficult these firms are finding it to shift their products.

It is not all doom and gloom for CSR, as there is an increasing number of success stories about the adoption of voice recognition telephony by firms outside of the healthcare sector. These firms are beginning to see reasonable returns on their investments.

Continuing research, improvements in the accuracy of natural sounding "continuous speech", and the involvement of established technology firms such as Microsoft, Apple and IBM, mean that it would not be long before applications would have widely acceptable errors rates within the healthcare sector and push itself towards becoming a key healthcare technology.



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