Real-Time Speech Transcription Using Context-Based Model Assignment
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Solution Overview
Problem
Current customer service call centers face inefficiencies and loss of contextual information due to difficulties in consumer interactions with automated and semi-automated systems, leading to high costs and ineffective data harvesting.
Innovation Solution
A computer-implemented method and system that electronically monitors telephonic interactions between consumers and service providers, assigning context-based speech recognition models in real-time to transcribe speech into text, using hierarchical models that adapt based on language, dialect, and transaction roles, and swapping models as necessary to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated and semi-automated systems are used for customer service interactions, then operational efficiency is improved, but contextual information is lost and interaction accuracy deteriorates
Solution Approach 1:
The patent replaces traditional automated voice recognition systems with an AI-powered natural language processing system that can understand contextual information, language nuances, and dialect variations, thereby maintaining operational efficiency while preventing loss of contextual information
Solution Approach 2:
The system dynamically adjusts speech recognition parameters based on detected language, dialect, and transaction role, allowing it to adapt to different interaction contexts and maintain high accuracy across diverse customer service scenarios
2Measurement precision
If context-based speech recognition models are assigned to different channels, then transcription accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the speech recognition system into multiple specialized models organized in a hierarchy, with each model trained for specific languages, dialects, or transaction roles, allowing high accuracy for each segment while managing overall system complexity through modular organization
Solution Approach 2:
The system dynamically selects and switches between different speech recognition models based on the detected context, language, and transaction role, enabling adaptive accuracy improvement without requiring all models to be active simultaneously, thus managing computational resources efficiently
3Adaptability or versatility
If hierarchical speech recognition models are used with multiple variants, then adaptability to different languages and roles is improved, but processing time increases
Solution Approach 1:
The patent pre-organizes speech recognition models in a hierarchical structure with language, dialect, and role variants ready for selection, and performs rapid context analysis at the beginning of each interaction to determine the appropriate model, avoiding time-consuming model switching during the interaction
Solution Approach 2:
The system automatically detects the language, dialect, and transaction role during the interaction and self-selects the appropriate speech recognition model without requiring manual intervention or extensive processing, enabling rapid adaptation to different contexts
Data Source
AI summary
A computer-implemented method and supporting system transcribes spoken words being monitored from a telephonic interaction among two or more individuals. Telephonic interactions among the individuals are monitored, and at least two of the individuals are each assigned to a separate channel. While still being monitored, each of the channels is assigned a context-based speech recognition models, and in substantially real-time, the monitored telephonic interaction is transcribed from speech to text based on the different assigned models.