Speaker identification aims at determining the speaker identity by analyzing his voice characteristics, and relies typically on statistical models or machine learning techniques. Frequency-domain features are by far the most used choice to encode the audio input in sound recognition. Recently, some studies have also analyzed the use of time-domain raw waveform (RW) with deep neural network (DNN) architectures. In this paper, we hypothesize that both time-domain and frequency-domain features can be used to increase the robustness of speaker identification task in adverse noisy and reverberation conditions, and we present a method based on a late fusion DNN using RWs and gammatone cepstral coefficients (GTCCs). We analyze the characteristics of RW and spectrum-based short-time features, reporting advantages and limitations, and we show that the joint use can increase the identification accuracy. The proposed late fusion DNN model consists of two independent DNN branches made primarily by convolutional neural networks (CNN) and fully connected neural networks (NN) layers. The two DNN branches have as input short-time RW audio fragments and GTCCs, respectively. The late fusion is computed on the predicted scores of the DNN branches. Since the method is based on short segments, it has the advantage of being independent from the size of the input audio signal, and the identification task can be computed by summing the predicted scores over several short-time frames. Analysis of speaker identification performance computed with simulations show that the late fusion DNN model improves the accuracy rate in adverse noise and reverberation conditions in comparison to the RW, the GTCC, and the mel-frequency cepstral coefficients (MFCCs) features. Experiments with real-world speech datasets confirm the efficiency of the proposed method, especially with small-size audio samples.
