Audio Silence Clustering for Accurate Text Punctuation
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Solution Overview
Problem
Existing methods for text punctuation from speech data require large amounts of training data and manual effort, and existing systems often introduce cascading errors due to reliance on textual features.
Innovation Solution
A method that identifies silent sections in audio input, groups them using clustering algorithms like k-means, and associates each section with punctuation marks based on expected distributions and temporal durations, reducing reliance on textual features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used to apply punctuation to text, then punctuation accuracy can be improved, but large amounts of training data are required which makes the process time-consuming and requires manual work
Solution Approach 1:
The patent extracts and utilizes acoustic features (specifically silence sections) directly from the audio input to determine punctuation, rather than relying on text-based machine learning models that require extensive training data. This extraction approach eliminates the need for large manually annotated training corpora while maintaining punctuation accuracy.
Solution Approach 2:
The patent introduces acoustic features as an intermediary between the audio input and punctuation determination. Instead of directly analyzing text to apply punctuation, the system uses acoustic characteristics (silence sections) as a mediator to infer punctuation points, thereby avoiding the need for text-based machine learning training.
2Extent of automation
If text-based machine learning models are used for punctuation, then punctuation can be applied automatically, but cascading errors occur due to reliance on textual features
Solution Approach 1:
The patent inverts the conventional approach by not using text features to determine punctuation, but rather using acoustic features from the original audio signal. This inversion avoids the propagation of errors from text recognition since the punctuation decisions are made independently based on acoustic silence patterns rather than textual analysis.
Solution Approach 2:
The patent replaces the text-based mechanical processing system with an acoustic-based system. Instead of processing text features through machine learning models, the system processes acoustic features (silence sections) directly from the audio signal to determine punctuation, substituting one processing domain for another more reliable domain.
Data Source
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AI summary
According to an embodiment, a computer- implemented method for punctuation of text from an audio comprises: obtaining an audio input comprising speech data; identifying a plurality of silent sections in the audio input; grouping the plurality of silent sections into a plurality of groups, wherein each group in the plurality of groups corresponds to a punctuation mark or a space without a punctuation mark; and associating each silent section in the plurality of silent sections with a punctuation mark or a space according to the group of the silent section, thus obtaining punctuation information.