Speech Recognition Post-Processing for Personalized Display Text
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Solution Overview
Problem
Existing speech recognition systems lack the ability to customize display post processing (DPP) to accommodate individual user preferences for lexical to display form transformations, leading to inefficient resource usage and suboptimal readability.
Innovation Solution
A customized multi-stage DPP pipeline that includes upstream and downstream filters straddling base model stages, allowing users to tailor the processing to their preferences without altering the model stages, using a baseline pipeline efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a one-size-fits-all DPP engine is used, then resource efficiency is improved, but user preference satisfaction deteriorates
Solution Approach 1:
The DPP system is segmented into multiple independent transformation stages (disfluency removal, capitalization, punctuation, date formatting). Each stage can be independently configured and activated based on user preferences, allowing the system to provide personalized processing without requiring separate DPP engines for each preference combination.
Solution Approach 2:
The DPP pipeline is designed to be dynamically configurable, where users can select which transformation stages to apply based on their preferences. The system adapts its processing behavior in real-time by activating or deactivating specific stages, enabling a single engine to serve multiple user preferences efficiently.
2Adaptability or versatility
If a customized DPP pipeline is created for each user preference combination, then user preference satisfaction is improved, but resource efficiency deteriorates
Solution Approach 1:
A single DPP engine is designed to perform multiple functions by incorporating all possible transformation stages within one system. This universal engine can be configured to handle any combination of user preferences without requiring separate specialized engines, thereby maintaining resource efficiency while providing personalized service.
3Ease of operation
If multiple transformation stages are applied, then readability is improved, but processing complexity increases
Solution Approach 1:
The complex transformation process is divided into discrete, manageable stages (disfluency removal, capitalization, punctuation, date formatting). Each stage handles a specific aspect of text improvement, making the overall complex process easier to manage, configure, and understand while maintaining high readability output.
Data Source
AI summary
Solutions for custom display post processing (DPP) in speech recognition (SR) use a customized multi-stage DPP pipeline that transforms a stream of SR tokens from lexical form to display form. A first transformation stage of the DPP pipeline receives the stream of tokens, in turn, by an upstream filter, a base model stage, and a downstream filter, and transforms a first aspect of the stream of tokens (e.g., disfluency, inverse text normalization (ITN), capitalization, etc.) from lexical form into display form. The upstream filter and/or the downstream filter alter the stream of tokens to change the default behavior of the DPP pipeline into custom behavior. Additional transformation stages of the DPP pipeline perform further transforms, allowing for outputting final text in a display format that is customized for a specific user. This permits each user to efficiently leverage a common baseline DPP pipeline to produce a custom output.