Multi-layer Speech Transcript Post-processing System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automatic speech recognition systems generate transcripts that can be confusing for humans and machines, leading to impaired understanding and decreased performance in downstream tasks such as machine reading comprehension and summarization, due to issues like abbreviations, disfluencies, and lack of contextual understanding.
Innovation Solution
A multi-layer transcript post-processing system comprising a data-driven statistical layer, a rule-based layer, a machine learning approach layer, and an optional human editor layer, which selects, refines, and adjusts transcripts to improve accuracy and readability, using techniques like neural model inference and human feedback to fine-tune the output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a standard automatic speech recognition model is used to generate transcripts, then the transcription process is efficient and automated, but the transcript quality deteriorates with abbreviations, disfluencies, and contextual errors that impair human understanding and machine processing
Solution Approach 1:
The system segments the transcript enhancement process into four distinct layers: data-driven statistical layer, rule-based layer, machine learning approach layer, and human editor layer. Each layer addresses specific types of errors and processes different aspects of transcript quality, allowing parallel and sequential processing that maintains efficiency while progressively improving accuracy
Solution Approach 2:
The system merges multiple diverse approaches (statistical models, rule-based systems, neural networks, and human editing) into a unified multi-layer architecture. This combination leverages the strengths of each approach while compensating for their individual weaknesses, achieving both high productivity and high precision
2Measurement precision
If transcripts are processed through multiple enhancement layers including human editing, then transcript quality and contextual understanding improve, but system complexity and processing time increase
Solution Approach 1:
By dividing the complex enhancement task into four specialized layers, each with specific functions and error types to address, the system manages complexity through modular design. This segmentation allows each component to remain relatively simple while the integrated system achieves high overall quality
Solution Approach 2:
The data-driven and rule-based layers perform preliminary error correction and normalization before transcripts reach the machine learning and human editing layers. This preliminary action reduces the burden on subsequent layers and enables earlier intervention on common errors
3Speed
If standard speech recognition transcripts are used for downstream tasks like machine reading comprehension, then processing speed is maintained, but task performance deteriorates due to errors and ambiguities in the transcripts
Solution Approach 1:
The multi-layer system performs preliminary enhancement of transcripts before they are used in downstream tasks. By correcting errors, resolving abbreviations, and improving contextual accuracy in advance, the system ensures that downstream tasks receive high-quality input, thereby improving their performance without sacrificing processing speed
Data Source
AI summary
According to some embodiments, a multi-layer speech recognition transcript post processing system may include a data-driven, statistical layer associated with a trained automatic speech recognition model that selects an initial transcript. A rule-based layer may receive the initial transcript from the data-driven, statistical layer and execute at least one pre-determined rule to generate a first modified transcript. A machine learning approach layer may receive the first modified transcript from the rule-based layer and perform a neural model inference to create a second modified transcript. A human editor layer may receive the second modified transcript from the machine learning approach layer along with an adjustment from at least one human editor. The adjustment may create, in some embodiments, a final transcript that may be used to fine-tune the data-driven, statistical layer.