AI Speech Summarization Using Bootstrapped Pseudolabels
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Solution Overview
Problem
The lack of available annotations hampers the ability to learn effective artificial intelligence classification models for speech signal processing, particularly in medical dialogues, due to the difficulty in annotating large datasets and the imbalance of discourse classes in medical conversations.
Innovation Solution
A method involving a bootstrapping component that uses heuristics to generate turn-level pseudolabels, followed by iterative refinement using a human-in-the-loop approach to improve label accuracy, and a summarization component that leverages GPT-3 and ensemble methods to generate high-quality synthetic training data for medical dialogue summarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets are annotated manually to train AI classification models, then model accuracy improves, but annotation time and cost increase significantly
Solution Approach 1:
The system uses automated bootstrapping where the AI model generates its own training data through heuristic-based pseudolabeling of speech transcripts. The model iteratively refines its own annotations without requiring extensive manual human annotation, thereby achieving high labeling accuracy while minimizing annotation time and cost.
Solution Approach 2:
The system performs preliminary automated labeling using heuristics and pseudolabels before final model training. This preliminary action creates an initial labeled dataset that can be quickly generated and then iteratively improved, avoiding the need to start from scratch with time-consuming manual annotation.
2Reliability
If more discourse classes are balanced in medical conversations, then model performance improves, but data collection and annotation complexity increase
Solution Approach 1:
The system introduces an intermediary bootstrapping component that uses heuristics to generate pseudolabeled data. This intermediary process balances discourse classes automatically by generating synthetic training examples for underrepresented classes, improving model performance without requiring complex manual data collection and balancing procedures.
3Measurement precision
If extensive human annotation is performed to improve label accuracy, then classification model effectiveness improves, but productivity decreases due to manual effort requirements
Solution Approach 1:
The system achieves high label accuracy through automated self-service mechanisms where the AI model generates its own training data via pseudolabeling and iterative refinement. This eliminates the need for extensive manual human annotation while maintaining high accuracy, thereby preserving processing efficiency and productivity.
Solution Approach 2:
The system implements iterative feedback loops where the model generates pseudolabeled data, evaluates its performance, and refines its labeling heuristics accordingly. This automated feedback mechanism continuously improves label accuracy without requiring additional manual annotation effort, maintaining high productivity.
Data Source
AI summary
An apparatus for speech signal processing using artificial intelligence comprises: a microphone configured to receive speech and convert the received speech to a digital speech signal; at least one processor; and a non-transitory computer-readable medium having stored thereon instructions to cause the least one processor to execute the method of speech signal processing using artificial intelligence. The method comprises: receiving the digital speech signal; converting the speech signal to text; labelling, with at least one machine learning model, components of the text; and generating, with the at least one machine learning model, with the labelled components, at least one of a care plan or summary.