Speech control in surgery: A field analysis and strategies

Abstract

This work introduces a robot driven camera controlled by speech. The SIMIS database of 20 recordings of real life surgical operations serves as basis for analyses and noise modelling. To overcome low recognition performance due to high noise levels during operations, the vocabulary was chosen to be highly limited and multiple noise reduction methods have been investigated. We show that the use of feature enhancement techniques, such as Histogram Equalization or a Switching Linear Dynamic Model capturing the dynamics of speech show a remarkable improvement in recognition accuracy. Considering a severe condition of usage of the recognition system with all appearing noise types, the mean accuracy can be raised from 89.67 % to 91.16 % with SLDM, and to 95.50 % with HEQ enhancement.

New technologies for information retrieval to achieve situational awareness and higher patient safety in the surgical operating room: the MRI institutional approach and review of the literature

“…Because speech recognition in the OR suffers from increased noise disturbance levels, a ''Switching Linear Dynamic Model'' for separated modeling of noise levels and speech can be used [40].…”

Speech recognition for embedded automatic positioner for laparoscope

“…Its utmost precise and reliable positioning is crucial for doctors' diagnosis and treatment 1 . The traditional hand-held operation is flexible, however, the camera view may be fuzzy and unstable because the laparoscope is sometimes focused incorrectly and vibrates due to the assistant's hand trembling.…”

Speech Recognition for Endoscopic Automatic Positioning System

“…Endoscopic surgery as opposed to open surgery offers distinct benefits as reduced pain, shorter hospitality and quicker convalescence to patients. The utmost precise and reliable positioning of the endoscope is the key to the surgery [1]. The traditional hand-held operation is flexible since commands are often executed by assistants.…”