# An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments
## 3 Proposed Framework  
Our proposed methodology uses two deep neural networks: a recurrent neural network (RNN) and a multilayer perceptron, also called a deep neural network with a softmax layer as its last layer. The RNN is used for learning features from the audio signal. In contrast, the deep neural network with softmax is used to identify the speaker (i.e., for classification) based on these features [34]. The RNN is chosen because audio signal processing is a sequence processing problem. We use a multilayer perceptron with a softmax activation function for the speaker recognition task. Voice activity detection (VAD) is used to remove noise from the input signal. A BiLSTM, which can also be used for sequence processing problems, is also put in place of the RNN to compare its performance with the proposed system. A softmax classifier is used as the last layer of the deep neural network speaker identifier because it will convert the readings coming from the hidden layers into a probability distribution in which the most probable item, i.e., the speaker, will be output. For noise robustness, a framing-based noise removal method is incorporated into the preprocessing phase of the system. During this process, which is called voice activity detection, only the frames which belong to the actual speaker are preserved, while the frames belonging to the noise are discarded. This methodology uses the spectrum flatness index and energy ratio index of the signal to decide whether the frame belongs to the speaker or the noise [16]. The threshold used in this decision will be selected as a hyperparameter of the system. The layout of the proposed methodology is shown in Figure 1.  
We use the Kaggle speaker recognition dataset. The dataset contains 16,000 samples to benchmark speaker recognition technology on single-speaker audio acquired under unconstrained conditions. The conditions represented in the dataset provide samples of five individuals in clean interviews and speech in different outdoor conditions.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Resolved Issues** & **Features Used** & \begin{tabular}{c} **Speaker Identification** \\ **Predictor** \\ \end{tabular} & **Dataset Used** & **Performance** \\ \hline [PERSON] et al. [30] & \begin{tabular}{c} Extensive computation \\ run-time while calculating \\ i-vectors \\ \end{tabular} & i-vectors & \begin{tabular}{c} Baum-Welth statistics \\ and \\ Subspace-Orthogonal \\ Prior (SOP) \\ \end{tabular} & NIST SRE 2010 & \begin{tabular}{c} SOP approach speeds \\ up i-vector calculation \\ considerably as \\ compared to standard \\ i-vector calculation \\ \end{tabular} \\ \hline [PERSON] et al. [31] & Speech separation & \begin{tabular}{c} time-frequency \\ representation of the \\ mixture signal \\ \end{tabular} & Deep learning & \begin{tabular}{c} _Wall Street Journal_ \\ (WS10) Street \\ dataset \\ \end{tabular} & \begin{tabular}{c} Comparable or better \\ performance than other \\ state-of-the-art deep \\ learning methods \\ \end{tabular} \\ \hline [PERSON] et al. [32] & \begin{tabular}{c} Text-dependent speaker \\ recognition with random \\ digit strings \\ \end{tabular} & i-vectors & HMM & \begin{tabular}{c} 1.52\% and 1.77\% equal \\ error rate (EE) for male \\ and female speakers, \\ respectively \\ \end{tabular} \\ \hline [PERSON] et al. [33] & \begin{tabular}{c} Discriminant speaker \\ embeddings for \\ short-duration speech \\ \end{tabular} & i-vectors & \
\begin{tabular}{c} Neural-network-based \\ deep discriminant \\ analysis (DDA) \\ \end{tabular} & SRE corpus &
\begin{tabular}{c} 30\% relative \\ EER reduction \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: The proposed framework.