GPT-Guided Neural Punctuation for Disfluency-Aware Speech Segmentation
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Solution Overview
Problem
Conventional automatic speech recognition systems struggle with accurately segmenting and punctuating spoken language utterances due to natural pauses and disfluencies, leading to incomplete sentences and degraded output quality in downstream applications.
Innovation Solution
A system that identifies disfluencies in decoded audio data and applies corrections to improve readability, using a teacher model to generate weak labels and fine-tune production models for optimal punctuation and segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If timeout-driven logic is used for audio segmentation, then the system is simple to implement, but sentences are chopped off in the middle leading to degraded output quality
Solution Approach 1:
The patent replaces the mechanical timeout-driven segmentation system with a neural network-based system that uses linguistic signals and acoustic information to determine segmentation points, thereby achieving more accurate sentence boundaries without manual configuration
Solution Approach 2:
The patent introduces an intermediary punctuator component that receives decoded segments and applies punctuation rules to identify sentence boundaries, acting as a mediator between the decoder and downstream applications to improve segmentation quality
2Manufacturing precision
If neural network-based models are used for audio segmentation, then segmentation accuracy is improved, but the system over-segments audio leading to incomplete sentences
Solution Approach 1:
The patent implements a feedback mechanism where the punctuator's segmentation decisions are evaluated and adjusted based on linguistic context and punctuation rules, allowing the system to learn from previous segmentation errors and improve sentence completeness
Solution Approach 2:
The patent modifies the segmentation parameters by incorporating punctuation-based boundary detection and linguistic signal analysis, changing the decision criteria from purely acoustic features to a combination of acoustic and linguistic features that better preserve sentence integrity
3Productivity
If conventional punctuation is applied to decoded segments, then processing speed is maintained, but punctuation accuracy degrades due to disfluencies and natural pauses
Solution Approach 1:
The patent applies preliminary punctuation marking based on linguistic signals and acoustic features before final segmentation, allowing the system to pre-identify potential sentence boundaries and disfluencies, thereby improving final punctuation accuracy without adding significant processing delay
Solution Approach 2:
The patent segments the punctuation task into multiple stages: initial punctuation marking, disfluency detection, and final refinement, allowing each stage to focus on specific aspects of the problem and collectively achieve higher overall accuracy
Data Source
AI summary
Some disclosed embodiments are directed to obtaining a decoded audio data including a spoken language utterance recognized in audio data and identifying a disfluency in the decoded audio data. Upon determining that correcting the disfluency would improve a readability score of the decoded audio data, the system generates a particular correction to correct the disfluency and applies the particular correction to the decoded audio data. Then, an updated decoded audio data is generated which reflects the particular correction. The updated decoded audio data has improved readability over the decoded audio data.