Contextual Speech Interpretation with Selective LLM Reprocessing
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Solution Overview
Problem
Existing digital assistant systems struggle to efficiently handle natural language inputs with disfluencies, corrections, or ambiguities, leading to inefficiencies and increased power consumption.
Innovation Solution
Incorporating large language models (LLM) with reference resolution systems to enhance contextual speech interpretation, enabling robust correction and modification of speech inputs, and leveraging LLM's training for specific datasets to improve user-device interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing digital assistant systems process natural language inputs with disfluencies and ambiguities, then they can handle user requests, but power consumption increases and processing efficiency decreases
Solution Approach 1:
The system segments speech processing into multiple stages: initial ASR processing, disfluency detection, targeted re-processing of ambiguous segments using LLM, and final intent resolution. This selective re-processing approach avoids full re-analysis of entire speech inputs, reducing computational overhead and power consumption while maintaining interpretation accuracy for problematic segments.
Solution Approach 2:
A disfluency detection module serves as an intermediary between ASR and final intent recognition. This intermediary identifies and flags ambiguous segments, allowing the system to focus computational resources only on problematic areas rather than processing entire speech inputs through multiple complex models, thereby reducing overall power consumption.
2Ease of operation
If existing digital assistant systems handle corrections and disfluencies in speech input, then they can maintain conversation flow, but processing time increases
Solution Approach 1:
The system divides speech processing into discrete segments and processes only those containing disfluencies or ambiguities through the LLM, rather than re-processing entire speech inputs. This segmented approach maintains natural language handling capability while significantly reducing the time required to resolve corrections and disfluencies.
Solution Approach 2:
The disfluency detection module performs preliminary identification of problematic segments before LLM processing. By pre-marking areas requiring correction or clarification, the system avoids unnecessary processing of clear segments, thereby reducing overall processing time while maintaining ease of operation for natural language inputs.
3Reliability
If reference resolution systems are used alone, then they can resolve spatial references efficiently, but they struggle with linguistic errors and disfluencies
Solution Approach 1:
The system merges reference resolution capabilities with LLM-based disfluency detection and correction. The LLM component handles linguistic errors and disfluencies that pure reference resolution systems cannot process, while the reference resolution system maintains efficiency for standard spatial references. This combination achieves both high reference resolution accuracy and adaptability to linguistic variations.
Solution Approach 2:
The LLM acts as an intermediary layer between raw speech input and reference resolution processing. It pre-processes and corrects linguistic errors, disfluencies, and ambiguities before passing cleaned segments to the reference resolution system, thereby enhancing the system's ability to handle linguistic variations while maintaining reference resolution efficiency.
Data Source
AI summary
An example process includes receiving a speech input from a user and obtaining a representation of the speech input. The process further includes in accordance with a determination that at least one entity within the representation requires disambiguation: adjusting the representation by adding an indication to the at least one entity, and providing the adjusted representation to a language model. The process further includes in accordance with a determination that the adjusted representation requires modification: modifying, by the language model, the adjusted representation, and providing an output based on the modified and adjusted representation.