The extraction of speech features is the basis of speech signal processing, and the extraction of speech features is to obtain the parameters representing speech signals through the analysis of speech signals. The shape information of the signal can be extracted and processed by selecting appropriate structural elements and adopting mathematical morphological transformation. Selecting different structural elements will result in different shape transformation results, thus extracting the shape information of different components. In this paper, the feature extraction and analysis of speech signals are further studied based on FRFT (fractional fourier transform). The simulation results show that in different noise environments, such as when the speech is bathed with signal-to-noise ratio of 0dB and 10dB respectively, the recognition rate of this method is higher than that of the traditional parametric method. In this paper, based on the strength of FRFT signal components, the component signals are detected one by one in order, and then according to the detection results, the strongest component signals are removed from the observed signals in order to reduce their influence on the weak component signals, thus improving the effectiveness and reliability of multi-component signal detection and parameter estimation.

Yueying Zhai. Feature Extraction and Analysis of Speech Signal Based on Fractional Fourier Transform. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 5: 107-112. https://doi.org/10.25236/AJCIS.2023.060515.

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