• Shaik Reshma Sultana
• Yedukondal
• Shaik Ahmed Alisha
• Shaik Ameer Basha

Speaker recognition is an important task in the field of speech processing. IN this paper,we compare the Radial Basis Function Neural Network with and the Backpropagation Neural Network two widely used methods for Speaker recognition. Both methods use MFCC feature extraction method to reduce the dimensionality of the input data before using the appropriate neural network model for classification. The proposed system was evaluated on a dataset consisting of 10 speakers, with each speaker providing 10 utterances. Our test findings demonstrate that the RBFNN technique outperformed the BPNN strategy in terms of accuracy. Namely, the accuracy of the RBFNN was 98.80%, whereas that of the BPNN was 94.80%.

Radial Basis Function Neural Network(RBFNN), Back Propagation Neural Network(BPNN), Mel Frequency cepstral Coefficients(MFCC ),Discrete Wavelet Transform(DWT).

"Speaker Recognition using RBFNN-MFCC-DWT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.a302-a306, April-2023, Available :http://www.jetir.org/papers/JETIR2304040.pdf

"Speaker Recognition using RBFNN-MFCC-DWT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppa302-a306, April-2023, Available at : http://www.jetir.org/papers/JETIR2304040.pdf