Abstract

Speaker recognition is one of the important tasks in the signal processing. In this study we perform speaker recognition using MFCC with ELM. First noise is removed in the speech through low pass filter; the purpose of the filter is to remove the noise below 4 kHz. After enhancement of individual speech, feature vector is formed through Mel-frequency Cepstral Coefficient (MFCC). It is one of the nonlinear cepstral coefficient function, features are extracted using DCT, Mel scale and DCT. The feature set is given to Extreme Learning Machine (ELM) for training and testing the individual speech for speaker recognition. Compared to other machine learning technique, ELM provides faster speed and good performance. Experimental result shows the effectiveness of the proposed method.

Keywords:

Discrete cosine transform, filter bank, mel-frequency cepstral coefficient , mel scale,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.