Punctuation-Aware Language Model for Real-Time Speech Transcription
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Solution Overview
Problem
Existing automatic speech recognition systems face challenges in accurately predicting and inserting non-verbalized punctuation marks during speech transcription, particularly in real-time applications, due to the need for separate language models and post-processing steps, which can lead to inaccuracies and increased complexity.
Innovation Solution
Integrating punctuation prediction with speech decoding using a punctuation-aware statistical language model, allowing for the prediction and insertion of non-verbalized tokens directly during the decoding process, rather than as a post-process, and utilizing a finite state machine abstraction layer to handle language models, enabling simultaneous detection and insertion of punctuation symbols without relying on acoustic evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If punctuation prediction is performed as a post-processing step after speech decoding, then the system can use separate language models for speech decoding and punctuation prediction, but this increases system complexity and processing time while potentially reducing prediction accuracy
Solution Approach 1:
The patent merges punctuation prediction with speech decoding by integrating punctuation tokens into the speech recognition vocabulary and using a single language model for both functions. This unified approach eliminates the need for separate punctuation prediction models and post-processing steps, thereby reducing system complexity while maintaining or improving punctuation prediction accuracy through joint optimization of both functions within one decoding framework
2Reliability
If punctuation prediction is performed as a post-processing step, then the system can process speech decoding and punctuation separately, but this increases processing time and reduces real-time performance
Solution Approach 1:
By combining punctuation prediction with speech decoding in a single pass through the language model, the system eliminates the sequential processing steps required when punctuation prediction is performed separately. This integrated approach allows both speech recognition and punctuation identification to occur simultaneously during the decoding process, significantly reducing total processing time and enabling real-time application
Solution Approach 2:
The system performs punctuation prediction preliminarily during the speech decoding process itself rather than defer it to a later stage. Punctuation tokens are predicted and inserted into the transcript stream concurrently with word recognition, ensuring that punctuation information is available immediately as part of the decoding output without requiring additional post-processing time
3Reliability
If separate language models are used for speech decoding and punctuation prediction, then each function can be optimized independently, but this increases computational resources and system complexity
Solution Approach 1:
The patent implements a universal language model that serves multiple functions: speech decoding, punctuation prediction, and phrase segmentation. This multi-functional model eliminates the need for separate specialized models for each task, reducing the total computational resources required while maintaining the ability to optimize both speech recognition and punctuation prediction through a single unified framework that processes both functions simultaneously
Data Source
AI summary
Non-verbalized tokens, such as punctuation, are automatically predicted and inserted into a transcription of speech in which the tokens were not explicitly verbalized. Token prediction may be integrated with speech decoding, rather than performed as a post-process to speech decoding.