Audio Segmentation Using Look-Ahead Acousto-Linguistic Scoring
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Solution Overview
Problem
Conventional automatic speech recognition systems segment audio based on timeout-driven logic, which often chops off words in the middle of a sentence, degrading the quality of output for downstream processing components.
Innovation Solution
The system uses smart audio segmentation by evaluating potential segmentation boundaries based on look-ahead acousto-linguistic features, generating acoustic and language segmentation scores, and determining whether to generate a segment break based on these scores and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If timeout-driven logic is used for audio segmentation, then the system operates with simple logic and fast processing, but the segmentation accuracy deteriorates and speech utterances are chopped off in the middle of sentences
Solution Approach 1:
The system performs preliminary actions by evaluating acoustic and language scores at potential segmentation boundaries before finalizing segmentation decisions. This allows the system to look ahead and assess whether a pause represents a true sentence boundary or merely a mid-sentence hesitation, thereby improving segmentation accuracy without overly complicating the processing pipeline
Solution Approach 2:
The patent introduces intermediary scoring mechanisms (acoustic segmentation scores and language segmentation scores) that act as mediators between the raw audio input and final segmentation output. These intermediate evaluations provide nuanced decision-making criteria beyond simple timeout logic, enhancing accuracy while maintaining manageable system complexity through modular score evaluation
2Measurement precision
If neural network-based models with current acoustic information and linguistic signals are used, then segmentation accuracy improves, but the system over-segments audio and creates unnecessary segment breaks
Solution Approach 1:
The system employs feedback mechanisms where acoustic segmentation scores and language segmentation scores are evaluated together to inform segmentation decisions. This feedback loop allows the system to correct for over-segmentation tendencies by considering both acoustic cues and linguistic context, ensuring that segment breaks are only created when both scores indicate a true boundary rather than a mid-sentence pause
Solution Approach 2:
The patent changes parameters by introducing dual-score evaluation (acoustic and language scores) with threshold-based decision making. This parameter change allows the system to distinguish between genuine sentence boundaries and mid-sentence pauses more effectively, reducing over-segmentation while maintaining high segmentation accuracy through coordinated threshold evaluations
3Measurement precision
If more look-ahead words are analyzed for segmentation decisions, then segmentation accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial action by analyzing a limited number of look-ahead words (typically 1-3 words) rather than processing the entire remaining audio stream. This partial evaluation provides sufficient context to distinguish sentence boundaries from mid-sentence pauses while avoiding the excessive processing time that would result from analyzing all subsequent audio content
Data Source
AI summary
Systems and methods are provided for smart audio segmentation using look-ahead based acousto-linguistic features. For example, systems and methods are provided for obtaining audio, processing the audio, identifying a potential segmentation boundary within the audio, and determining whether to generate a segment break at the potential segmentation boundary. One or more look-ahead words occurring after the potential segmentation boundary are identified, wherein an acoustic segmentation score and a language segmentation score associated with the potential segmentation boundary and the one or more look-ahead words are generated. Systems then either refrain from generating a segment break at the potential segmentation boundary or generate the segment break at the potential segmentation boundary based on the acoustic and/or language segmentation score at least meeting or exceeding a segmentation score threshold.