Speech Recognition Segmentation for Audio Efficiency
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Solution Overview
Problem
Existing speech recognition technologies face inefficiencies due to excessive resource consumption and reduced accuracy when processing audio data that includes non-speech content, as they perform feature extraction and recognition on entire audio files without segmenting speech from other sounds.
Innovation Solution
A method and apparatus for speech recognition that involves acquiring audio data, determining the start and end times of speech segments, extracting these segments, and performing recognition on them separately to generate recognition text, thereby improving accuracy and efficiency by isolating speech from other audio components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition is performed on entire audio files without segmentation, then the process is simpler, but resource consumption increases and accuracy decreases
Solution Approach 1:
The patent applies segmentation by dividing the audio file into multiple audio segments based on time intervals. Each segment is then independently processed for speech recognition. This segmentation allows the system to focus computational resources only on segments containing speech, thereby reducing overall resource consumption while improving recognition accuracy by isolating speech from non-speech content.
Solution Approach 2:
The patent extracts speech segments from the entire audio file by identifying and isolating portions that contain speech based on recognition results. This extraction process removes non-speech content from the processing pipeline, reducing the amount of data that requires intensive computational analysis and thereby reducing resource consumption while maintaining high recognition accuracy.
2Productivity
If speech recognition is performed on entire audio files, then no preprocessing is needed, but processing time increases
Solution Approach 1:
The patent segments the audio file into multiple smaller audio segments that are processed in parallel or sequentially. This segmentation reduces the processing time for each individual segment compared to processing the entire audio file as a single unit, thereby improving overall speech recognition speed while the systematic approach minimizes total processing time through efficient resource utilization.
Solution Approach 2:
The patent performs preliminary actions by first dividing the audio file into segments and identifying which segments contain speech before performing full speech recognition. This preliminary segmentation and identification step prevents unnecessary processing of non-speech segments, thereby reducing overall processing time while maintaining high recognition speed for actual speech content.
3Reliability
If feature extraction is performed on all audio data, then no data loss occurs, but computational complexity increases
Solution Approach 1:
The patent segments the audio data into multiple smaller units and performs feature extraction only on segments identified as containing speech. This segmentation approach maintains recognition reliability by ensuring that speech segments are processed with full attention, while reducing computational complexity by avoiding feature extraction on non-speech segments where it would be unnecessary.
Solution Approach 2:
The patent extracts and isolates speech segments from the entire audio file before performing feature extraction. This extraction process removes non-speech content from the computational pipeline, thereby reducing the overall computational complexity of feature extraction while maintaining high recognition reliability by focusing computational resources exclusively on speech-containing segments.
Data Source
AI summary
Embodiments of the disclosure disclose a method and apparatus for speech recognition, an electronic device and a medium. The method includes: acquiring an audio data to be recognized (201), the audio data to be recognized including a speech segment; determining a start and end time corresponding to the speech segment which is comprised in the audio data (202); extracting at least one speech segment from the audio data to be recognized based on the determined start and end time (203); and performing speech recognition on the at least one extracted speech segment to generate recognition text corresponding to the audio data to be recognized (204).