GPT-Guided Neural Punctuation for Disfluent Speech
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Solution Overview
Problem
Conventional speech recognition systems struggle with accurately segmenting and punctuating spoken language utterances due to natural pauses and disfluencies, leading to incomplete sentences and degraded transcription quality in downstream applications.
Innovation Solution
A system that uses a disfluency tagger to identify and correct disfluencies in decoded audio data, employing a teacher model to generate weak labels and fine-tune a student model for improved punctuation, ensuring complete sentences are output while retaining intentional fragments.
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 due to natural pauses
Solution Approach 1:
The patent replaces the mechanical timeout-driven segmentation system with a neural network-based semantic segmentation system. The neural network analyzes linguistic signals and acoustic information to identify sentence boundaries, substituting the simple timeout mechanism with an intelligent model that understands natural language structure, thereby resolving the contradiction between implementation simplicity and segmentation accuracy.
Solution Approach 2:
The patent changes the segmentation parameters from fixed timeout values to dynamic semantic features extracted by the neural network. Instead of using fixed time thresholds, the system adjusts segmentation decisions based on variable linguistic cues such as sentence endings, pauses, and contextual meaning, allowing accurate segmentation that adapts to different speaking patterns.
2Manufacturing precision
If neural network-based models are used for segmentation, then segmentation accuracy improves, but the system over-segments audio leading to incomplete sentences
Solution Approach 1:
The patent implements a feedback mechanism where the neural network's segmentation decisions are evaluated and refined. The system uses the decoded segment and linguistic signals to adjust segmentation boundaries, ensuring that complete sentences are identified and output. This feedback loop prevents over-segmentation by continuously refining boundaries based on sentence completion criteria.
Solution Approach 2:
The patent performs preliminary processing of audio data through decoding and linguistic signal extraction before final segmentation decisions are made. By preparing the data in advance and identifying potential sentence boundaries through multiple cues, the system can make more accurate segmentation decisions that preserve sentence completeness while maintaining high accuracy.
3Productivity
If conventional punctuators are used, then processing speed is maintained, but punctuation accuracy degrades due to disfluencies
Solution Approach 1:
The patent replaces conventional rule-based punctuators with a neural network-based punctuation system. This substitution enables the system to handle disfluencies and varied speech patterns more effectively while maintaining processing speed through optimized neural network inference, resolving the contradiction between speed and accuracy in punctuation.
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.