مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

279
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

0
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

Information Journal Paper

Title

A New and Efficient Feature Extraction Method for Robust Speech Recognition Based on Fractional Fourier Transform and Differential Evolution Optimizer

Pages

  85-96

Abstract

 One of the main challenges in speech recognition is noise resistant feature extraction. In this paper, a new feature extraction algorithm, called Fractional and Adaptive Power Normalized Cepstral Coefficients Algorithm, has been proposed as a noise-resistant method for speech recognition. This proposed feature extraction method is based on a fractional short-term Fourier Transform. The selection of fractional conversion coefficient is important for proper analysis of multi-component signals like speech. Therefore, the proposed method obtains the optimum parameter of α for Fractional Fourier Transform based on the noise class in the environment, adaptively by the Differential Evolution meta-heuristic algorithm. Moreover, TI Digit and Noisex-92 are used for evaluation of the resistance and accuracy of the recognition of the automatic speech recognition system. Simulation results show more resistance and higher recognition accuracy of the proposed feature extraction method rather than other methods in noisy and without noise environments. In the proposed ASR system, the Support Vector Machine (SVM) Classifier with a nonlinear kernel has been used. Also, all the simulations are performed in MATLAB.

Cites

  • No record.

    • References

  • No record.

    • Cite

    APA: Copy

    SADEGHI, MOHSEN, MARVI, HOSSEIN, & AHMADYFARD, ALIREZA. (2020). A New and Efficient Feature Extraction Method for Robust Speech Recognition Based on Fractional Fourier Transform and Differential Evolution Optimizer. JOURNAL OF MODELING IN ENGINEERING, 18(61 ), 85-96. SID. https://sid.ir/paper/405188/en

    Vancouver: Copy

    SADEGHI MOHSEN, MARVI HOSSEIN, AHMADYFARD ALIREZA. A New and Efficient Feature Extraction Method for Robust Speech Recognition Based on Fractional Fourier Transform and Differential Evolution Optimizer. JOURNAL OF MODELING IN ENGINEERING[Internet]. 2020;18(61 ):85-96. Available from: https://sid.ir/paper/405188/en

    IEEE: Copy

    MOHSEN SADEGHI, HOSSEIN MARVI, and ALIREZA AHMADYFARD, “A New and Efficient Feature Extraction Method for Robust Speech Recognition Based on Fractional Fourier Transform and Differential Evolution Optimizer,” JOURNAL OF MODELING IN ENGINEERING, vol. 18, no. 61 , pp. 85–96, 2020, [Online]. Available: https://sid.ir/paper/405188/en

    Related Journal Papers

  • No record.

    • Related Seminar Papers

  • No record.

    • Related Plans

  • No record.

    • Recommended Workshops






    Move to top