AI Captioning Ensemble for Speech Rate Adaptation
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Solution Overview
Problem
Current auto-captioning methods suffer from high error rates due to variations in speech characteristics and background noise, failing to meet the 98.6% accuracy requirement, especially when the rate of speech exceeds 120-160 words per minute, which is optimal for speech-to-text recognition.
Innovation Solution
The system processes input audio to adjust its tempo to fall within the 120-160 words per minute range using an ensemble method, generating new audio files that are then used to improve caption accuracy by employing AI algorithms to predict and integrate captions into audiovisual content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the rate of speech is increased to convey information faster, then productivity improves, but measurement precision deteriorates because speech-to-text recognition accuracy drops when rate exceeds 120-160 words per minute
Solution Approach 1:
The system performs preliminary audio processing to extract acoustic features and predict word timings before final caption generation. By pre-processing the audio signal to identify speech characteristics and segment potential words, the system prepares optimized data structures that enable accurate recognition even at higher speech rates, addressing the accuracy-speed tradeoff through advance preparation
Solution Approach 2:
The system introduces an intermediary processing layer that includes audio feature extraction, word timing prediction, and caption generation as separate stages. This intermediary pipeline allows the system to decouple the speech input from the text output, enabling optimization at each stage independently and maintaining accuracy while handling variable speech rates
2Adaptability or versatility
If background noise is present in the audio input, then the system must process more complex signals, but reliability deteriorates due to high error rates in caption generation
Solution Approach 1:
The system extracts and separates the relevant speech signal from the background noise by focusing on acoustic features specific to human speech. By extracting only the pertinent audio characteristics needed for word recognition and filtering out irrelevant noise components, the system maintains high caption accuracy even in noisy environments
Solution Approach 2:
The system employs feedback mechanisms where predicted word timings and captions are continuously refined based on the extracted acoustic features. The prediction model adjusts its output based on the quality and characteristics of the input audio, allowing it to compensate for noise and maintain reliable caption generation across varying audio conditions
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for generating captions is provided. The present invention may include capturing input audio comprising audiovisual content; processing the input audio to extract an input rate of speech, input word timings, and input word predictions; generating one or more new audio files by altering the input rate of speech of the input audio to fall within a pre-determined range; processing the one or more new audio files to extract new word timings and a new word predictions; creating a mapping that pairs the input word timings with corresponding new word timings; selecting a word prediction for each paired input word timing and new word timing based on the mapping; and integrating the selected word predictions into the audiovisual content for display.