Audio Analysis System for Semantic and Non-Semantic Feature Extraction
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Solution Overview
Problem
Conventional speech recognition solutions primarily focus on semantics, missing non-semantic characteristics like intonation, emotion, and sarcasm, which limits their ability to accurately convey the intended meaning in human-machine interactions.
Innovation Solution
The system analyzes audio by segmenting it into utterance and noise segments, extracting semantic and non-semantic features, including laughter detection, emotion recognition, and sentence boundary identification, using predictive models like neural networks and support vector machines to construct a transcript that displays these characteristics and their relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition solutions only account for semantics, then the system complexity is reduced and processing is simpler, but the accuracy of understanding the true meaning is insufficient
Solution Approach 1:
The patent segments the audio signal into multiple characteristic components including semantic content, prosodic features, paralinguistic features, and contextual information. Each segment is processed separately by dedicated analysis modules, allowing comprehensive understanding without overwhelming system complexity. This segmentation enables parallel processing of different audio characteristics.
Solution Approach 2:
The system employs a multi-functional analysis framework that simultaneously extracts and integrates multiple types of information from the same audio input. The predictive model serves multiple functions by analyzing semantic, prosodic, and paralinguistic features in unison, rather than requiring separate systems for each type of analysis.
2Loss of information
If the system captures both semantic and non-semantic characteristics, then the completeness of information is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent introduces prosodic features as an intermediary layer that bridges semantic content and paralinguistic characteristics. These intermediate features facilitate the detection and measurement of both semantic and non-semantic aspects by providing a common analytical framework that integrates multiple information types.
Solution Approach 2:
The system replaces traditional mechanical signal processing methods with machine learning-based predictive models that automatically detect and measure complex audio characteristics. These intelligent systems handle the difficulty of detecting multiple characteristics simultaneously by learning patterns from training data rather than relying on manual feature engineering.
3Loss of information
If conventional solutions only transcribe audio into words, then the processing speed is maintained, but the level of speech understanding remains at basic semantics
Solution Approach 1:
The system performs preliminary extraction of prosodic and paralinguistic features during the audio processing stage, before final interpretation. This preliminary action prepares multiple layers of information in advance, enabling faster comprehensive understanding without requiring slower sequential analysis of each characteristic after transcription.
Data Source
AI summary
Various embodiments of the invention provide methods, systems, and computer-program products for analyzing an audio to capture semantic and non-semantic characteristics of the audio and corresponding relationships between the semantic and non-semantic characteristics. In particular embodiments, the audio is segmented into a set of utterance segments containing a party speaking on the audio and a set of noise segments containing the party not speaking on the audio. The semantic and non-semantic characteristics are then captured for each of the utterance segments. Specifically, speech analytics is performed on each segment to identify the words spoken by the party in the segment as semantic characteristics. Further, laughter, emotion, and sentence boundary detection is performed on each segment to identify occurrences of such in the segment as non-semantic characteristics. Once identified for each segment, various embodiments of the invention involve constructing a transcript based on the identified semantic and non-semantic characteristics.