Speech Transcript Punctuation from Audio Pause Clustering
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Solution Overview
Problem
Existing methods for obtaining punctuation in text from speech data require large amounts of training data and manual effort, making them time-consuming and inefficient.
Innovation Solution
A computer-implemented method that identifies silent sections in audio input, groups them using clustering algorithms like k-means, and associates each section with a punctuation mark based on expected distributions, reducing the need for extensive training data.
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 the requirement for large amounts of training data increases
Solution Approach 1:
The patent extracts punctuation information directly from the audio signal by analyzing silent sections (pauses) between spoken words. Instead of using machine learning models that require large training datasets, the system identifies punctuation marks by detecting temporal patterns - specifically, silent sections that correspond to punctuation pauses in the audio stream. This extraction approach eliminates the need for extensive training data while maintaining punctuation accuracy.
Solution Approach 2:
The patent replaces the machine learning-based mechanical system with an acoustic signal processing approach. Instead of using neural networks or statistical models that process text data, the system directly analyzes the audio waveform to identify silent sections. This substitution of the processing mechanism allows the system to obtain punctuation information without requiring large quantities of training data, as it derives punctuation cues directly from the acoustic signal's temporal structure.
2Measurement precision
If machine learning models are trained for punctuation, then punctuation quality improves, but manual work and time consumption increase
Solution Approach 1:
The system performs self-service by automatically detecting punctuation marks through acoustic signal analysis without requiring manual annotation of training data. The algorithm autonomously identifies silent sections in the audio signal, groups them into punctuation candidates, and applies punctuation rules to generate the final punctuation markup. This self-service approach eliminates the time-consuming manual data collection and annotation process while maintaining high punctuation quality through systematic acoustic pattern recognition.
3Productivity
If silent sections are grouped using clustering algorithms, then punctuation identification efficiency improves, but algorithm complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the audio signal into discrete silent sections (pauses) between spoken words. Each silent section is then independently analyzed and grouped based on its temporal characteristics. This segmentation approach simplifies the overall problem by breaking down the continuous audio stream into manageable units that can be systematically processed through clustering algorithms, improving identification efficiency while keeping the algorithm complexity manageable through structured processing steps.
Data Source
AI summary
Disclosed herein is a computer-implemented method for punctuation of text from audio. The method includes 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, where 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 grouping of the silent sections, thus obtaining punctuation information.