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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Multitaper MFCC Features for Acoustic Stress Recognition from Speech

Author 1: Salsabil Besbes Author 2: Zied Lachiri
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 8, No. 3 · Published 2017 · Cited by 5

DOI: https://doi.org/10.14569/IJACSA.2017.080361

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Abstract

Ameliorating the performances of speech recognition system is a challenging problem interesting recent researchers. In this paper, we compare two extraction methods of Mel Frequency Cepstral Coefficients used to represent stressed speech utterances in order to obtain best performances. The first method known as traditional is based on single window (taper) generally the Hamming window and the second one is a novel technique developed with multitapers instead of a single taper. The extracted features are then classified using the multiclass Support Vector Machines. Experimental results on the SUSAS database have shown that the multitaper MFCC features outperform the conventional MFCCs.

Keywords

Mel Frequency Cepstral Coefficients (MFCC) Multitapering Multiclass SVM Stress recognition

How to Cite this Article

Besbes, S., & Lachiri, Z. (2017). Multitaper MFCC Features for Acoustic Stress Recognition from Speech. International Journal of Advanced Computer Science and Applications, 8(3). https://doi.org/10.14569/IJACSA.2017.080361

Besbes, Salsabil, and Zied Lachiri. "Multitaper MFCC Features for Acoustic Stress Recognition from Speech." International Journal of Advanced Computer Science and Applications, vol. 8, no. 3, 2017, https://doi.org/10.14569/IJACSA.2017.080361.

@article{Besbes2017,
  title     = {Multitaper MFCC Features for Acoustic Stress Recognition from Speech},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {3},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Salsabil Besbes and Zied Lachiri},
  doi       = {10.14569/IJACSA.2017.080361},
  url       = {https://doi.org/10.14569/IJACSA.2017.080361}
}

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