Speech Signal Segmentation via Frequency-Domain Energy Correlation
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Solution Overview
Problem
Existing speech signal segmentation methods have low accuracy due to the characteristics of phonemes and strong noise, as they rely on sudden changes in time-domain energy, which may not always occur.
Innovation Solution
The method processes speech signals using frequency-domain energy distributions, obtaining frequency-domain energy ratios and derivatives to calculate correlation coefficients between frames, allowing for accurate segmentation based on spectral changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If segmentation is performed based on sudden changes in time-domain energy, then the segmentation process is simple, but the segmentation accuracy deteriorates when phoneme characteristics or strong noise are present
Solution Approach 1:
The patent transforms the segmentation approach from time-domain energy analysis to frequency-domain energy distribution analysis. By calculating the energy distribution across different frequency bands and analyzing inter-frame correlations in the frequency domain, the method detects spectral changes that indicate phoneme boundaries, thereby improving segmentation accuracy without significantly increasing system complexity
Solution Approach 2:
The patent changes the parameter space from time-domain energy values to frequency-domain energy distribution parameters. By computing energy ratios across multiple frequency bands and their inter-frame correlations, the method captures spectral characteristics that are more reliable for detecting phoneme boundaries, especially in noisy conditions where time-domain energy changes are unreliable
2Measurement precision
If frequency-domain energy distribution is used for segmentation, then segmentation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the frequency spectrum into multiple bands and computes energy distribution independently for each band. By segmenting the frequency domain and analyzing inter-frame correlations of energy ratios across these bands, the method achieves accurate phoneme boundary detection while keeping computational requirements manageable through parallel processing of frequency bands
Solution Approach 2:
The patent introduces intermediate parameters including frequency-domain energy ratios and inter-frame correlation coefficients as mediators between the raw frequency-domain signals and the final segmentation decisions. These intermediate representations capture essential spectral change information while reducing computational complexity compared to direct analysis of full frequency spectra
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves the accuracy of speech signal segmentation by effectively identifying spectral changes, even in the presence of noise, leading to more precise phoneme and syllable recognition.
Implementation Method 1
an FFT unit which performs FFT on an input signal and calculates a power spectrum component for each frame
Data Source
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AI summary
A method and an apparatus for processing a speech signal according to frequency-domain energy are provided. According to the method and apparatus for processing a speech signal according to frequency-domain energy: an original speech signal including a first speech frame and a second speech frame that are adjacent to each other is received (101); a Fourier Transform is performed on the first speech frame and the second speech frame separately to obtain a first frequency-domain signal and a second frequency-domain signal (102); a frequency-domain energy distribution of the first speech frame and a frequency-domain energy distribution of the second speech frame are obtained (103); a frequency-domain energy correlation coefficient between the first speech frame and the second speech frame is obtained (104); and the original speech signal is segmented according to the frequency-domain energy correlation coefficient (105). A problem that a speech signal segmentation result has low accuracy due to a characteristic of a phoneme of a speech signal or severe impact of noise when refined speech signal segmentation is performed can be resolved.