Voice Characteristic Integration for NLU Intent Accuracy
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Solution Overview
Problem
Current speech recognition systems lack the ability to accurately interpret user intent by considering voice characteristics such as sentiment, uncertainty, and age, leading to inadequate responses and user experiences.
Innovation Solution
The system integrates voice characteristics detection to update natural language understanding hypotheses based on voice data, using trained models to adjust interpretations and generate appropriate responses, ensuring a more informed analysis beyond plain text analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems use only plain text analysis, then the system complexity is low, but the accuracy of user intent interpretation is insufficient
Solution Approach 1:
The system segments voice analysis into distinct components: text transcription, voice characteristic extraction (sentiment, uncertainty, age), and integrated hypothesis evaluation. This segmentation allows each component to be processed independently and combined to achieve comprehensive intent interpretation without overwhelming system complexity.
Solution Approach 2:
The system transitions from two-dimensional text-only analysis to three-dimensional analysis by adding voice characteristic dimensions (sentiment, uncertainty, age) to the traditional text transcription dimension. This dimensional expansion enables more accurate intent interpretation while maintaining manageable complexity through modular processing.
2Loss of information
If the system integrates voice characteristics detection, then the understanding of user sentiment improves, but the processing time increases
Solution Approach 1:
The system performs preliminary voice characteristic extraction during the audio processing phase, before full NLU hypothesis evaluation. By pre-computing sentiment, uncertainty, and age characteristics from voice data, the system avoids redundant processing during the interpretation phase, reducing overall processing time while maintaining comprehensive sentiment understanding.
Solution Approach 2:
The voice characteristic detection system uses self-contained models that independently analyze audio features without requiring extensive external processing. The sentiment, uncertainty, and age detection models operate autonomously on the voice data, enabling parallel processing that minimizes time loss while capturing comprehensive user sentiment information.
Data Source
AI summary
A system is provided for using voice characteristics in determining a user intent corresponding to an utterance. The system processes a NLU hypothesis and voice characteristics data, using a trained model, to determine an alternate NLU hypothesis based on the voice characteristics data. The voice characteristics data may indicate if a user's level of uncertainty when speaking the utterance, an age group of the user, a sentiment of the user when speaking the utterance, and other data.