The role of automated speech recognition in endoscopic data collection.
Speech recognition technology has developed substantially in the past half decade. Currently, large vocabulary, speaker independent, discrete recognizers are the state-of-the-art. This will change. Moderate sized, continuous recognition systems now exist in research settings. However, it is unlikely that such systems will be widely available until the mid to late 1990's. The accuracy rates of current speech recognition systems are high. Consequently, speech accuracy is not the current limiting aspect of using ASR. The limiting aspect of using ASR technology is the approach to integrating speech functionality into applications. One approach is to use ATNs as models of natural language to support both an input strategy and a text generation system. ATNs provide approaches to both syntactical correctness and semantic richness. This is an approach which plays to the strengths of the discrete nature of current speech technology and also provides a methodology for the capture and archiving of highly detailed information. The ATN approach avoids the natural language parsing problem created by a fully free form dictation interface. Evolving along with the underlying speech technology are standards in the definitions and criteria used in endoscopic practice. There are clear benefits from standards in this area. However, it is likely that this will also take several years and may never yield a universally accepted lexicon. Furthermore, there will be user interface barriers to surmount in any system attempting to use speech as an input modality. Because of the relatively large vocabularies used in medical discourse, the user interface will need to be carefully crafted.(ABSTRACT TRUNCATED AT 250 WORDS)
Johannes
R S
RS
Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Carr-Locke
D L
DL
D003625:Data Collection / Q000379:methods*; D004724:Endoscopy; D010363:Pattern Recognition, Automated; D013060:Speech; D014825:Vocabulary; D014831:Voice