Streaming Punctuation with Layered Segmentation for Long Dictation
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Solution Overview
Problem
Conventional automatic speech recognition systems face issues with inaccurate audio segmentation, particularly for slow speakers or those who pause mid-sentence, leading to premature segmentation and grammatical errors in transcriptions.
Innovation Solution
A layered segmentation approach using a transformer-based punctuation model applies initial and final segmentation boundaries, allowing for correction of initial errors and ensuring accurate sentence segmentation by considering bi-directional context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If time-out based segmentation is used, then processing speed is improved, but segmentation accuracy deteriorates
Solution Approach 1:
The patent segments the transcription process into multiple phases: initial transcription with temporary segmentation boundaries, then refinement phase with final segmentation boundaries. This allows the system to provide fast initial results while continuously improving accuracy through iterative refinement, resolving the contradiction between processing speed and segmentation accuracy.
Solution Approach 2:
The system dynamically adjusts segmentation boundaries through multiple passes. Initial boundaries are set based on speaker pauses and punctuation models for fast processing, then boundaries are dynamically revised in subsequent passes based on improved acoustic models and linguistic context, achieving both speed and accuracy through adaptive refinement.
2Measurement precision
If neural network based models are used, then segmentation accuracy is improved, but over-segmentation occurs
Solution Approach 1:
The patent implements feedback mechanisms where the system monitors transcription quality and uses this feedback to adjust segmentation boundaries. Punctuation models and acoustic models provide continuous feedback to refine boundaries, preventing over-segmentation by correcting premature breaks identified through linguistic analysis and speaker behavior patterns.
Solution Approach 2:
The system changes segmentation parameters dynamically across multiple passes. Initial passes use one set of parameters for fast transcription, then parameters are adjusted (such as minimum pause duration, punctuation probability thresholds) in subsequent passes to reduce over-segmentation and improve transcription reliability while maintaining accuracy.
3Loss of time
If segmentation occurs at speaker pauses, then response time is reduced, but grammatical errors increase
Solution Approach 1:
The system performs preliminary segmentation based on speaker pauses and punctuation models to provide quick initial transcription results. Then, in refinement passes, the system applies additional analysis considering linguistic context and speaker behavior patterns to correct grammatical errors, achieving both fast response time and high grammatical precision through staged processing.
Solution Approach 2:
Segmentation boundaries are dynamically adjusted based on linguistic context and speaker behavior analysis. The system initially segments at obvious pauses for fast processing, then dynamically revises boundaries by analyzing sentence structure, punctuation probability, and speaker intent to correct grammatical errors while maintaining rapid response.
Data Source
AI summary
Systems generate segments of spoken language utterances based on different sets of segmentation boundaries. The systems are also configured to generate one or more formatted segments by assigning a punctuation tags at segmentation boundaries and to generate one or more final sentences from the one or more segments.