Audio Anomaly Detection and Truncation for Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Audio recordings often contain anomalies such as busy signals and extended periods of silence due to incomplete hang-up signals, leading to resource wastage and crashes in speech recognition engines, with existing solutions being inefficient and requiring additional hardware or software.
Innovation Solution
A system and method that detect and repair audio recordings by identifying clusters of silence, determining the length and location of anomalies, and truncating the recordings at specific points to remove these anomalies, without the need for expensive hardware or software, by analyzing amplitude in digital audio samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If speech recognition engine processes audio files with anomalies, then processing continues, but system reliability deteriorates due to crashes and poor acoustic signatures
Solution Approach 1:
The system performs preliminary detection and removal of anomalies (busy signals, hang-up sounds, extended silence) from audio files before they reach the speech recognition engine. By preprocessing the audio to eliminate harmful elements in advance, the system ensures reliable engine operation while maintaining continuous transcription processing.
2Loss of information
If transcription center records continuously without detecting hang-up, then recording completeness improves, but audio quality deteriorates due to inclusion of anomaly portions
Solution Approach 1:
The system extracts and removes anomaly portions (busy signals, hang-up sounds, extended silence) from the recorded audio files while preserving the valuable dictation content. This extraction process separates harmful elements from useful information, delivering clean audio for transcription while maintaining complete capture of the original speech content.
3Measurement precision
If manual review of audio files is performed to detect anomalies, then detection accuracy improves, but processing time increases substantially
Solution Approach 1:
The system replaces manual mechanical review with automated electronic detection using signal processing algorithms. The automated system analyzes audio files for anomaly patterns (busy signals, hang-up sounds, extended silence) with high accuracy while processing files rapidly without human intervention, eliminating the time loss associated with manual inspection.
Data Source
AI summary
A system and method is disclosed for detecting and repairing audio recordings that contain busy signals and extended periods of silence by searching for clusters of silence by reviewing the amplitude in an audio recording sample and listing each silence and sample time.