As early as 1975 speech recognition systems were described ‘in which isolated words, spoken by a designed talker, are recognized through calculation of a minimum prediction residual’ reporting a 97.3 per cent recognition rate for a male speaker.
Speech recognition (SR) systems compose of microphones (converting sound into electrical signals), sound cards (that digitalise the electrical signals) and speech engine software (that convert the data into text words). Speech recognition, in particular, presents some interesting applications. Language technologies hold the potential for making information easier to understand and access. Health informatics or eHealth solutions enable clinical data to become potentially accessible through computer networks for the purposes of improving health outcomes for patients and creating efficiencies for health professionals. Today, a literature search using Pubmed for computational linguistics, natural language processing, human language technologies, or text mining recovers over 20,000 references. Highlights of the 1990s and early 2000s include the MedLEE Medical Language Extraction and Encoding System to parse patient records and map them to a coded medical ontology and the Autocoder system to generate medical diagnosis codes from a patient record. Pioneering studies relating to technologies for producing and using written or spoken text, known as computational linguistics, natural language processing, human language technologies, or text mining, were published in the 1970s and 1980s. Technologies focusing on the generation, presentation and application of clinical information in healthcare, referred to as health informatics or eHealth solutions have experienced substantial growth over the past 40 years. SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. Fourteen studies met the inclusion criteria and were retained. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references.
Experimental and non-experimental designs were considered.
Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. MethodsĪ systematic review of existing literature from 2000 was undertaken. To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care.