A Hybrid Approach to Improving Automatic Speech Recognition Via NLP
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Using domain knowledge about medications to correct recognition errors in medical report creation
Louhi '10 Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents
Semantic and phonetic automatic reconstruction of medical dictations
Computer Speech and Language
Combination of error detection techniques in automatic speech transcription
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Hi-index | 0.00 |
Automated speech recognition (ASR) in radiology report dictation demands highly accurate and robust recognition software. Despite vendor claims, current implementations are sub-optimal, leading to poor accuracy, and time and money wasted on proofreading. Thus, other methods must be considered for increasing the reliability and performance of ASR before it is a viable alternative to human transcription. One such method is post-ASR error detection, used to recover from the inaccuracy of speech recognition. This thesis proposes that detecting and highlighting errors, or areas of low confidence, in a machine-transcribed report allows the radiologist to proofread more efficiently. This, in turn, restores the benefits of ASR in radiology, including efficient report handling and resource utilization. To this end, an objective classification of error-detection methods for ASR is established. Under this classification, a new theory of error detection in ASR is derived from the hybrid application of multiple error-detection heuristics. This theory is contingent upon the type of recognition errors and the complementary coverage of the heuristics. Inspired by these principles, a hybrid error-detection application is developed as proof of concept. The algorithm relies on four separate artificial-intelligence heuristics together covering semantic, syntactic; and structural error types, and developed with the help of 2700 anonymised reports obtained from a local radiology clinic. Two heuristics involve statistical modeling: pointwise mutual information and co-occurrence analysis. The remaining two are non-statistical techniques: a property-based, constraint-handling-rules grammar, and a conceptual distance metric relying on the ontological knowledge in the Unified Medical Language System. When the hybrid algorithm is applied to thirty real-world radiology reports, the results are encouraging: up to a 24% increase in the recall performance and an 8% increase in the precision performance over the best single technique. In addition, the resulting algorithm is efficient and modular. Also investigated is the development necessary to turn the hybrid algorithm into a real-world application suitable for clinical deployment. Finally, as part of an investigation of future directions for this research, the greater context of these contributions is demonstrated, including two applications of the hybrid method in cognitive science and machine learning. Keywords. medical informatics, automatic speech recognition, natural language processing, hybrid error detection, computer-assisted editing, radiology reporting