ASR Error Correction with Confidence and Red-Flag Context
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Solution Overview
Problem
Traditional automatic speech recognition (ASR) systems suffer from a word error rate of approximately 10-20%, leading to inefficiencies in training and failing to accurately identify certain categories, such as fraud, due to inaccurate transcriptions.
Innovation Solution
A system utilizing machine learning models (MLMs) to predict and correct ASR errors by comparing transcribed words to predefined confidence levels, generating predicted words, calculating numerical distances, and identifying red flag words or synonyms within the context of the transcription to classify the transcription as associated with specific categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ASR systems are used for transcription, then the system is simple and fast to operate, but the word error rate is high (10-20%) leading to inaccurate transcriptions
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the ASR system and the final transcription output. The MLM receives the ASR transcription, identifies potential errors by comparing predicted words with transcribed words, and generates corrected transcriptions. This intermediary layer resolves the contradiction by significantly improving transcription accuracy while adding only moderate system complexity through the integration of the MLM.
2Productivity
If traditional ASR systems are used, then the system operates quickly, but training efficiency is reduced due to inaccurate transcriptions
Solution Approach 1:
The system implements a feedback mechanism where the machine learning model continuously evaluates ASR transcriptions, identifies errors, and generates corrections. The corrected transcriptions are then used for training purposes, creating a feedback loop that improves training efficiency. By feeding back the corrected information to the training process, the system resolves the contradiction between training efficiency and transcription accuracy.
3Reliability
If traditional ASR systems are used, then the system is easy to operate, but fraudulent activity cannot be reliably identified
Solution Approach 1:
The machine learning model performs preliminary action by proactively identifying and correcting potential errors in ASR transcriptions before the transcriptions are used for fraud detection or other critical analyses. By preemptively correcting errors rather than reacting to them later, the system improves fraud detection reliability while maintaining manageable complexity through the use of established NLP techniques.
Data Source
AI summary
A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.