Voice Recognition Optimization via Talkgroup Context Segmentation
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Solution Overview
Problem
Existing voice recognition systems in noisy environments, such as those encountered by public safety organizations, are inaccurate due to the complexity of natural language and the inability to differentiate between similar terms and contexts.
Innovation Solution
A method and system that utilize a call controller to determine context data and create a list of talkgroup-specific keywords, processing audio data to generate initial output terms and matching them with talkgroup-specific keywords, thereby improving voice recognition and information searching within talkgroups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice recognition systems process audio data in noisy environments using general keywords, then they can handle a broad range of queries, but recognition accuracy deteriorates due to context ambiguity and similar terms
Solution Approach 1:
The patent segments the keyword space into talkgroup-specific keywords and general keywords. The system creates a dedicated list of talkgroup-specific keywords (first characteristic) that are relevant to the particular talkgroup's context, separating them from general vocabulary. This segmentation allows the system to prioritize context-relevant terms during voice recognition, improving accuracy without sacrificing broad query handling capability.
Solution Approach 2:
The patent applies local quality by assigning different keyword lists to different talkgroups based on their specific contexts. Each talkgroup receives customized keywords tailored to their operational domain (e.g., fire, medical, police), rather than using a uniform keyword list. This localized approach enables the system to adapt to local context requirements while maintaining overall system versatility.
2Measurement precision
If the system creates talkgroup-specific keyword lists based on context data, then voice recognition accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-processing audio data from talkgroup communications to automatically generate context data and create talkgroup-specific keyword lists before voice recognition occurs. The system analyzes historical communications, identifies relevant terms and patterns, and builds customized keyword lists in advance. This preliminary preparation reduces the complexity of real-time keyword management during active voice recognition operations.
Solution Approach 2:
The system applies self-service by automatically generating and updating talkgroup-specific keyword lists without requiring manual intervention. The call controller continuously monitors talkgroup communications, extracts context data, and autonomously maintains the keyword lists. This self-service mechanism reduces the operational burden and complexity of keyword list management while maintaining high recognition accuracy.
3Measurement precision
If the system processes audio data through multiple stages (initial output term generation and keyword matching), then recognition precision improves, but processing time increases
Solution Approach 1:
The patent applies partial action by implementing a two-stage processing approach where the system first generates initial output terms from audio data, then performs targeted matching against talkgroup-specific keywords. Rather than processing all possible keywords equally, the system focuses computational resources on the most relevant talkgroup-specific keyword list, achieving high precision without excessive processing time. The matching process stops once a sufficient number of relevant terms are identified.
Data Source
AI summary
A method and system for optimizing voice recognition and information searching. The method includes determining context data associated with a particular talkgroup (140) that includes a plurality of communications devices (120) and creating a list of talkgroup-specific keywords associated with the context data, the list of talkgroup-specific keywords including a first characteristic for each talkgroup-specific keyword. The method also includes receiving, from a first communications device (120A) of the plurality of communications devices (120), audio data associated with a user of the first communications device (120A) and processing the audio data to generate an initial output term. The method further includes determining a second characteristic of the initial output term and determining whether the first characteristic of a talkgroup-specific keyword from the list of talkgroup-specific keywords matches the second characteristic of the initial output term. The method also includes outputting the keyword when the first characteristic matches the second characteristic.