Audio Origin Analysis for Detecting Non-Semantic Speech States
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Solution Overview
Problem
Current AI voice recognition technologies lack the algorithms and data to understand, analyze, and predict non-semantic information in speech, limiting their application in fields that rely on audio information beyond semantic content.
Innovation Solution
An audio analytics system configured with artificial intelligence infrastructure to capture and analyze audio sources, identifying potential origin characteristics such as physical, mental, or emotional states of the source, applicable in various settings including conversations, security assessments, and healthcare environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current AI voice recognition technologies are used, then semantic aspects of speech can be understood with high accuracy, but non-semantic information such as emotion prosody and physical states cannot be detected
Solution Approach 1:
The patent divides the audio analysis task into separate processing channels: one for semantic content analysis and another for non-semantic prosodic features. The system segments the audio signal to independently analyze semantic components (words, syntax) from non-semantic components (pitch, rhythm, intensity), allowing both types of information to be processed simultaneously without interference.
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges semantic and non-semantic analysis. This intermediate component extracts prosodic features from the audio signal and combines them with semantic analysis results, enabling the system to infer physical and emotional states while maintaining accurate semantic understanding.
2Reliability
If AI models are trained only on semantic speech data, then speaker identification and verification work well, but the models fail to detect emotional states and physical conditions
Solution Approach 1:
The patent creates a universal audio analysis framework that serves multiple functions simultaneously. The same system can perform speaker identification, emotion detection, physical state monitoring, and semantic understanding by applying different analysis algorithms to the same audio input, making the model adaptable to various applications without requiring separate specialized systems.
Solution Approach 2:
The patent changes the parameters used for training and analysis by incorporating both semantic and non-semantic features into the model. Instead of training only on linguistic patterns, the system learns to recognize prosodic variations, pitch contours, and temporal patterns that indicate emotional and physical states, thereby expanding the model's detection capabilities while maintaining speaker identification reliability.
3Measurement precision
If humans analyze speech for non-verbal characteristics, then emotional and physical states can be detected, but humans cannot accurately understand such information themselves
Solution Approach 1:
The patent replaces human subjective interpretation with an automated computational system that objectively measures prosodic features. Instead of relying on human analysts who may misinterpret emotional states, the system uses algorithmic analysis of audio parameters (pitch, rhythm, intensity) to detect physical and emotional conditions with consistent accuracy, substituting human judgment with machine-based measurement.
Data Source
AI summary
The present disclosure provides for an audio analytics system. The audio analytics system may comprise one or more audio sources. The audio analytics system may comprise one or more training sources. The audio analytics system may use the training sources to generate an amount of training data that may be used to train at least one artificial intelligence infrastructure of the audio analytics system. The audio analytics system may comprise at least one audio capture device. The audio analytics system may be configured to receive at least one audio source via the audio capture device to enable the artificial intelligence infrastructure to execute at least one operation on the audio source to identify one or more potential origin characteristics associated with an origin of the audio source.