Extracting speech spectrogram of speech signal based on generalized S-transform

In speech signal processing, time-frequency analysis is commonly employed to extract the spectrogram of speech signals. While many algorithms exist to achieve this with high-quality results, they often lack the flexibility to adjust the resolution of the extracted spectrograms. However, applications such as speech recognition and speech separation frequently require spectrograms of varying resolutions. The flexibility of an algorithm in providing different resolutions is crucial for these applications. This paper introduces the generalized S-transform, and explains its fundamental theory and algorithmic implementation. By adjusting parameters, the proposed method flexibly produces spectrograms with different resolutions, offering a novel and effective approach to obtain speech signal spectrograms. The algorithm enhances the traditional Stockwell transform (S-transform) by incorporating a low-pass filtering function and introducing two adjustable parameters. These parameters modify the Gaussian window function of the basic S-transform, resulting with the generalized S-transform with customizable time-frequency resolution. Finally, this paper presents simulation experiments using both synthesized signals and real speech datas, comparing with the generalized S-transform with several commonly used spectrogram extraction algorithms. The experiments demonstrate that the generalized S-transform is feasible and effective, particularly when it is combined with the generalized fundamental frequency profile. The results indicate that this method is a viable and effective in obtaining spectrograms of speech signals, and has potential application in speech feature extraction and speech recognition. The pure speech dataset used in the experiments is sourced from a downloadable database and partially from a recorded speech set.