# An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments
## 2 Related Work  
Several techniques have been introduced to automate the speaker identification process. [PERSON] et al. [10] proposed an i-vector-based system for the speaker identification task. An i-vector-based method implies a low-dimensional representation of speech signals with different durations. In i-vector-based frameworks, session compensation is the first stage, before classification. Session compensation refers to methods adopted to deal with the conflation of things such as channel properties and the choice of spoken words with speaker characteristics [18]. The work in [17] proposes joint optimization of session compensation and the classifier. This work uses sparse coding (SC) for session compensation and softmax plus support vector machine (SVM) classifiers for the classification task. The system has been tested on King-ASR-010, VoxCeleb, and RSR2015 datasets. King-ASR-010 and RSR2015 are Chinese and Mandarin speech corpora that have labeled examples. This work is based on supervised learning, while that in Audeep is unsupervised learning, which is a plus in the case of unavailability of labeled data [19]. However, the work in [17] is compared with classifiers in the i-vector framework only.  
The work presented in [19] was also organized in the form of a framework that was evaluated on three public datasets, the CHAINS, LapsBM1.4, and YouTube datasets. This framework uses ranked lists, which encode similarity information defined by the speaker model. As a model, Gaussian Mixture Models (GMMs), vector quantization, and i-vector techniques are used, whereas, for learning, RLSim and ReckNN algorithms are used. [PERSON] [19] proposed a methodology for smart devices to recognize speakers based on a very short utterance. The work uses an i-vector and GMM-based approach which was tested on the THUYG-20 dataset and claimed to have an equal error rate (EER) of 3.21 percent. A novel GMM-based speaker identification technique is proposed in [21] which uses two statistical estimations. The novel GMM deals with noise. The system is tested on the NIST 2000 dataset and is claimed to have a 16 percent relative improvement over i-vector-based speaker identification methods.  
In [22], the authors provided an exhaustive systematic review for the identification, comparison, and analysis of feature extraction methods and algorithms found in the research literature spanning the period 2011-2016. This review shows that MFCC-based feature extraction methods are applied more as compared to other methods. The importance of paralinguistic information such as gender, age group, language, accent, and identity of the speaker in a speech signal is dealt with in [23]. This work compares the Gaussian Mixture Model-Universal Background Model (GMM-UBM), GMM-Support Vector Machine (GMM-SVM), and i-vector-based approaches. This work has a very interesting finding, i.e., \"For speaker recognition, error rate decreases as age increases\".  
[PERSON] et al. [23] proposed a methodology for speaker identification based on Gaussian Mixture Models (GMM) and the i-vector method with two features, PNCC (Power-Normalized Cepstral Coefficients) and RASTA PLP (Relative Spectral Perceptual Linear Prediction) coefficients. As is shown by Table 1, the most widely used features are i-vectors, whereas the most widely used classifiers are GMM, PLDA, and deep neural networks. In [26], the authors proved that Convolutional Neural Networks (CNN) outperform GMM and ResNet features and self-attention features outperform i-vectors.  
\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