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Results for “Speech Recognition Software”
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Lost in translation.
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[High-tech operating room for trauma surgery].
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Listen to the web. Widespread usage of voice-enabled web sites is a not-so-distant possibility.
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The current state of speech recognition. Despite high accuracy and growing interest, behavioral barriers remain.
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Can you hear me? Voice recognition technology helped Erlanger Health System improve interaction with payers.
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Press 1 to promote health behavior with interactive voice response.
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Interactive voice response telephone calls to enhance bone mineral density testing.
OBJECTIVE: Bone mineral density (BMD) testing is a key tool used to diagnose and treat osteoporosis. We assessed the rate of scheduling BMD tests among health plan members at risk for osteoporosis who received interactive voice response (IVR) calls. STUDY DESIGN: Cohort study. METHODS: Study patients included persons age 45 years with either a prior fracture or 90 days of glucocorticoid use and all women age 65 years during the 2-year baseline period. The IVR call provided educational content and then offered members an opportunity to transfer to schedule a BMD test. The primary outcome was scheduling a BMD test. RESULTS: We targeted 1402 health plan members, and 708 (50%) were successfully contacted. Of 54 patients who transferred to schedule a BMD test, only 3 actually did so. Because so few patients scheduled a BMD test, predictors of transfer were examined as a secondary end point. In a multivariate model, only self-reported intention to schedule a BMD test was a significant predictor (odds ratio = 4.4, 95% confidence interval = 2.2, 8.8). Members' age, sex, history of a prior fracture, self-report of a BMD test in the previous 2 years, acknowledgement of barriers to BMD testing, and discussion of BMD testing with one's physician were not related to transferring to schedule a BMD test. CONCLUSION: A letter and an IVR call prompted few to schedule a BMD test. More interventions to improve BMD testing should be developed and tested.
A novel voice signature method for electronic death registration systems.
Electronic signature of documents is frequently a challenging problem in the medical domain. This is particularly an issue with death certificates which require timely signatures. In the process of developing an electronic death registration system, we devised a novel remote signature method involving an off-the-shelf interactive voice response system (IVRS). This paper describes this system and its motivations.
Talking points. New technologies overcome delay, jitter and packet loss to ensure reliable VolP services across the data network.
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Developing and evaluating a wireless speech-and-touch-based interface for intelligent comprehensive triage support systems.
Continuous speech recognition (CSR) technology appears promising in mobile nursing but is not yet well studied. We developed and evaluated bimodal CSR and touchscreen triage support systems in the Emergency department (ED) of a medical center with 2700 beds in 2004-5. Evaluation results show that the average accuracy rates of systems ranged from 94 to 98%. Results suggest that the ED nurses were significantly more willing to use CSR combined with touchscreen systems than the others, such as PDA and CSR alone. A more flexible interface, not efficiency, might be the main reason for this finding.
An enterprising answer: case history.
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Is now the time for speech recognition?
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A continuous-speech interface to a decision support system: I. Techniques to accommodate for misrecognized input.
OBJECTIVE: Develop a continuous-speech interface that allows flexible input of clinical findings into a medical diagnostic application. DESIGN: The authors' program allows users to enter clinical findings using their own vernacular. It displays from the diagnostic program's controlled vocabulary a list of terms that most closely matches the input, and allows the user to select the single best term. The interface program includes two components: a speech-recognition component that converts utterances into text strings, and a language-processing component that matches recognized text strings with controlled-vocabulary terms. The speech-recognition component is composed of commercially available speech-recognition hardware and software, and developer-created grammars, which specify the language to be recognized. The language-processing component is composed of a translator, which extracts a canonical form from both recognized text strings and controlled-vocabulary terms, and a matcher, which measures the similarity between the two canonical forms. RESULTS: The authors discovered that grammars constructed by a physician, who could anticipate how users might speak findings, supported speech recognition better than did grammars constructed programmatically from the controlled vocabulary. However, this programmatic method of grammar construction was more time efficient and better supported long-term maintenance of the grammars. The authors also found that language-processing techniques recovered some of the information lost due to speech misrecognition, but were dependent on the completeness of supporting synonym dictionaries. CONCLUSIONS: The authors' program demonstrated the feasibility of using continuous speech to enter findings into a medical application. However, improvements in speech-recognition technology and language-processing techniques are needed before natural continuous speech becomes an acceptable input modality for clinical applications.
Voice recognition and aphasia: can computers understand aphasic speech?
PURPOSE: This preliminary investigation of the use of voice recognition software by people with aphasia presents a new method of training the software, which allows the aphasic user to by-pass the linguistically demanding standard training. It examines how accurately voice recognition software performed with aphasic speech and language as compared to that of control subjects. METHOD: Five non-impaired controls and six participants with aphasia used a vocabulary of 50 words and 24 phrases to train the software over a maximum of five sessions. For the people with aphasia baseline assessments provided a profile of speech and language impairment. Measures of software accuracy were taken for all subjects at baseline and after four subsequent training sessions, for both single word and phrase level dictation. RESULTS: Word level production resulted in similar accuracy levels for both groups. Phrase level production showed greater accuracy levels than single word level production for both aphasics and controls, but participants with greater speech difficulties had lower software accuracy scores. CONCLUSIONS: Training the software on a specific vocabulary allows people to access it whose speech and language difficulties would otherwise have prevented them. Findings are discussed in relation to use of the software as a dictation tool and as an input device to therapy software.
Widening the temporal window: processing support in the treatment of aphasic language production.
Investigations of language processing in aphasia have increasingly implicated performance factors such as slowed activation and/or rapid decay of linguistic information. This approach is supported by studies utilizing a communication system (SentenceShaper) which functions as a "processing prosthesis." The system may reduce the impact of processing limitations by allowing repeated refreshing of working memory and by increasing the opportunity for aphasic subjects to monitor their own speech. Some aphasic subjects are able to produce markedly more structured speech on the system than they are able to produce spontaneously, and periods of largely independent home use of SentenceShaper have been linked to treatment effects, that is, to gains in speech produced without the use of the system. The purpose of the current study was to follow up on these studies with a new group of subjects. A second goal was to determine whether repeated, unassisted elicitations of the same narratives at baseline would give rise to practice effects, which could undermine claims for the efficacy of the system.
A speech-controlled environmental control system for people with severe dysarthria.
Automatic speech recognition (ASR) can provide a rapid means of controlling electronic assistive technology. Off-the-shelf ASR systems function poorly for users with severe dysarthria because of the increased variability of their articulations. We have developed a limited vocabulary speaker dependent speech recognition application which has greater tolerance to variability of speech, coupled with a computerised training package which assists dysarthric speakers to improve the consistency of their vocalisations and provides more data for recogniser training. These applications, and their implementation as the interface for a speech-controlled environmental control system (ECS), are described. The results of field trials to evaluate the training program and the speech-controlled ECS are presented. The user-training phase increased the recognition rate from 88.5% to 95.4% (p<0.001). Recognition rates were good for people with even the most severe dysarthria in everyday usage in the home (mean word recognition rate 86.9%). Speech-controlled ECS were less accurate (mean task completion accuracy 78.6% versus 94.8%) but were faster to use than switch-scanning systems, even taking into account the need to repeat unsuccessful operations (mean task completion time 7.7s versus 16.9s, p<0.001). It is concluded that a speech-controlled ECS is a viable alternative to switch-scanning systems for some people with severe dysarthria and would lead, in many cases, to more efficient control of the home.
[Automated speech recognition for the generation of medical records in computed tomography].
PURPOSE: A study was performed to compare the performance of automatic speech recognition (ASR) with conventional transcription. MATERIALS AND METHODS: 100 CT reports were generated by using ASR and 100 CT reports were dictated and written by medical transcriptionists. The time for dictation and correction of errors by the radiologist was assessed and the type of mistakes was analysed. The text recognition rate was calculated in both groups and the average time between completion of the imaging study by the technologist and generation of the written report was assessed. A commercially available speech recognition technology (ASKA Software, IBM ViaVoice) running on a personal computer was used. RESULTS: The time for the dictation using digital voice recognition was 9.4 +/- 2.3 min compared to 4.5 +/- 3.6 min with an ordinary Dictaphone. The text recognition rate was 97% with digital voice recognition and 99% with medical transcriptionists. The average time from imaging completion to written report finalization was reduced from 47.3 hours with medical transcriptionists to 12.7 hours with ASR. The analysis of misspellings demonstrated (ASR vs. medical transcriptionists): 3 vs. 4 for syntax errors, 0 vs. 37 orthographic mistakes, 16 vs. 22 mistakes in substance and 47 vs. erroneously applied terms. CONCLUSIONS: The use of digital voice recognition as a replacement for medical transcription is recommendable when an immediate availability of written reports is necessary.