Automatic Speaker Identification System based MLP Network and Deep Learning in the Presence of Severe Interference

Abstract

This paper proposes two models for Automatic Speaker Identification (ASI) task in the presence of interference. The first one uses the Normalized Pitch Frequency (NPF) and Mel- Frequency Cepstral Coefficients (MFCCs) as extracted features and multi-layer perceptron (MLP) as a classifier. In this model, we investigate the utilization of the Discrete Transform (DT) such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sin Transform (DST) to increase the robustness of extracted features against different types of degradation through exploit the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This achieved by extracting features directly from speech signal contaminated with features extracted from Discrete transformed signals to create a hybrid feature vectors. The enhancement techniques such as Spectral Subtraction (SS), Winer Filter (WF) and Adaptive Wiener Filter as a preprocessing stage to eliminate the effect of the interference on the ASI performance. In the second model, we investigate the using of Deep Learning (DL) based Convolutional Neural Network (CNN) with the spectrogram and the Radon transform to increase the robustness of the ASI against the interference effects. One of this paper goals is making comparison between two models and reach to more robust ASI system against severe interference. The experimental results indicates that the two proposed models articulate satisfying results, the model-based CNN consuming time is less than that based MLP which requires large training epochs and using many techniques for feature extraction and enhancing performance.