ASR Subtitle Condensation for Readable Real-Time Spoken Text
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems produce unformatted, uncapitalized, and unpunctuated text that is difficult to read and often includes filler words and hesitations, making it unsuitable for real-time subtitles, especially in fast-paced live content, and requires a system to condense text while preserving meaning.
Innovation Solution
A neural network-based system using an encoder-decoder architecture, trained with human-edited transcripts and synthetic data, removes filler words and hesitations, and condenses text to a readable format with proper casing and punctuation, incorporating inverse text normalization for numbers and dates, and adjusts length based on speaking rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If ASR systems produce verbatim transcripts of spoken content, then all speech information is captured, but the text becomes too long and includes filler words making it unsuitable for real-time subtitles
Solution Approach 1:
The system extracts and removes filler words, hesitations, and non-essential elements from the verbatim transcript while preserving the core informational content. This selective extraction allows the subtitle to convey the essential meaning without the verbosity of the original speech, resolving the contradiction between information completeness and reading speed.
Solution Approach 2:
Instead of trying to make the speaker speak slower to match reading speed, the system inverts the approach by processing the fast speech through an encoder-decoder model that automatically condenses it to the appropriate length. The model learns to produce shorter, more readable output from longer input, effectively inverting the speed mismatch problem.
2Device complexity
If ASR systems output raw unformatted text, then processing is simple, but the text is difficult to read and not acceptable for TV stations
Solution Approach 1:
The system merges multiple processing functions into a single encoder-decoder model: formatting (capitalization, punctuation), condensation (removing filler words), and inverse text normalization (converting spoken numbers and dates to written form). This unified approach maintains relative processing simplicity while achieving comprehensive text improvement for TV broadcast quality.
Solution Approach 2:
The encoder-decoder model performs multiple functions simultaneously: it formats the text, condenses it, corrects errors, and applies inverse text normalization. This multi-functional model resolves the contradiction by handling all text improvement tasks in one processing pass, maintaining efficiency while dramatically improving readability.
3Speed
If speakers talk faster in live unscripted content, then content delivery is efficient, but the audience cannot read the subtitles at the same speed
Solution Approach 1:
The system dynamically adjusts the subtitle length based on the speech rate and timing information from the ASR system. The encoder-decoder model processes the input with attention mechanisms that adapt to the varying speed of speech, producing condensed output that matches the temporal constraints of the video content while maintaining readability.
Solution Approach 2:
The model changes the parameter of text length to resolve the speed mismatch. By controlling the output length parameter of the encoder-decoder model, the system produces subtitles that are proportionally shorter than the spoken input, effectively scaling the text to match the speech rate and allowing simultaneous viewing and reading without forcing the speaker to slow down.
4Productivity
If the text is condensed too much, then reading speed is improved, but important information may be lost
Solution Approach 1:
The encoder-decoder model uses attention mechanisms that provide feedback loops during processing, allowing it to monitor which parts of the input contain important information and ensure they are preserved in the output. The model learns from training data to maintain information completeness while condensing, automatically adjusting the level of condensation based on the semantic importance of different input segments.
Data Source
AI summary
A speech condensation processing system and method includes an ASR system for a source language that receives an audio stream with speech and outputs at least one word sequence and time stamps in the language spoken, a memory that stores a condensation program and corresponding data and databases that store training data, which may include manually condensed data, two-way translated data, and aligned subtitle data, and a processor coupled to the ASR system and memory that executes the condensation program to format and condense text by transforming the at least one word sequence from ASR into human-readable text with proper casing and punctuation, and condenses the text based neural training to remove words from the at least one word sequence that are not relevant for meaning.