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Extracting speech spectrogram of speech signal based on generalized S-transform
1College of Computer Science and Technology, Xinjiang University, Urumqi, Xinjiang, China.
Plos One
|January 13, 2025
Summary
This study introduces the generalized S-transform for flexible speech spectrogram analysis. It allows adjustable time-frequency resolution, enhancing speech recognition and feature extraction.
Area of Science:
- Signal Processing
- Time-Frequency Analysis
Background:
- Traditional spectrogram extraction methods often lack adjustable time-frequency resolution.
- Speech recognition and separation applications require spectrograms with varying resolutions.
Purpose of the Study:
- Introduce the generalized S-transform for flexible speech spectrogram generation.
- Enhance the traditional Stockwell transform (S-transform) with adjustable parameters.
Main Methods:
- Developed the generalized S-transform by incorporating a low-pass filter and two adjustable parameters.
- Modified the Gaussian window function of the S-transform for customizable time-frequency resolution.
Main Results:
- The generalized S-transform flexibly produces spectrograms with different resolutions.
- Demonstrated feasibility and effectiveness through simulations with synthesized and real speech data.
Conclusions:
- The generalized S-transform is a viable and effective method for obtaining speech signal spectrograms.
- This method shows potential for speech feature extraction and speech recognition, especially with the generalized fundamental frequency profile.
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