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Related Experiment Video

Updated: Jul 16, 2026

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Published on: April 11, 2026

Improving the utility of speech recognition through error detection.

Kimberly Voll¹, Stella Atkins, Bruce Forster

¹School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A1S6, Canada. kvoll@cs.sfu.ca

Journal of Digital Imaging

|March 28, 2007

Summary

Current speech recognition for radiology reports is inaccurate. A new statistical method for post-transcription error detection shows promise, achieving up to 96% accuracy in identifying errors and improving radiologist efficiency.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

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Published on: April 11, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Medical imaging and informatics
Natural Language Processing
Health data science

Background:

Speech recognition technology (SRT) has potential in radiology reporting but suffers from poor accuracy.
Inconsistent SRT performance leads to significant radiologist time spent correcting erroneous reports.
Current transcription methods are resource-intensive due to error correction needs.

Purpose of the Study:

To address the limitations of current SRT in radiology.
To develop and evaluate a post-transcription error detection method.
To enhance the efficiency of radiology report proofreading.

Main Methods:

A statistical method was developed for error detection.
The method is designed for application after speech recognition transcription.
The approach focuses on identifying inaccuracies in transcribed radiology reports.

Main Results:

The proposed statistical method demonstrated encouraging performance.
Error detection rates as high as 96% were achieved in specific cases.
The method shows potential for improving proofreading efficiency.

Conclusions:

Post-speech-recognition error detection offers a viable solution to SRT inaccuracies.
The developed statistical method can significantly assist radiologists in proofreading.
This approach promises to improve the reliability and efficiency of radiology reporting.