Speech Recognition DPP Pipeline for User-Specific Display Output
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Solution Overview
Problem
Existing speech recognition systems lack the ability to customize display post processing (DPP) to meet individual user preferences for lexical to display form transformations, leading to inefficiencies and resource waste in generating multiple DPP engines for varying user preferences.
Innovation Solution
A customized multi-stage DPP pipeline that utilizes upstream and downstream filters to alter the behavior of a baseline DPP pipeline, allowing users to efficiently generate personalized output by applying user-specific rules, while maintaining privacy and reducing computing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a one-size-fits-all DPP engine is used, then resource usage is efficient, but user preference customization is lost
Solution Approach 1:
The DPP pipeline is segmented into multiple independent transformation stages (e.g., date formatting, time formatting, capitalization, punctuation). Each stage can be independently configured and activated based on user preferences, allowing selective customization without processing all transformations unnecessarily.
Solution Approach 2:
The DPP engine dynamically adjusts its behavior by activating or deactivating specific transformation stages based on user preferences. The system transitions from a static one-size-fits-all approach to a dynamic configuration where only necessary transformations are applied, optimizing resource usage while maintaining customization.
2Adaptability or versatility
If multiple DPP engines are generated for different user preferences, then user preference customization is achieved, 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 unified system. The engine can adapt its behavior to serve different user preferences by selectively activating appropriate stages, eliminating the need for multiple separate DPP engines.
Solution Approach 2:
The unified DPP engine dynamically reconfigures itself based on user preferences, activating only the necessary transformation stages for each user's specific needs. This dynamic adaptation allows one engine to replace multiple specialized engines, reducing system complexity.
3Ease of operation
If a customized DPP pipeline is implemented, then user preference satisfaction improves, but system complexity increases
Solution Approach 1:
The DPP pipeline is divided into discrete, modular transformation stages that can be independently configured. This segmentation allows users to customize their experience by selecting specific stages without overwhelming system complexity, as each stage is self-contained and clearly defined.
Solution Approach 2:
A configuration layer acts as an intermediary between user preferences and the DPP transformation stages. This intermediary manages the complexity by providing a standardized interface for user customization while handling the intricate details of pipeline configuration internally.
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.