# An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments
## 1 Introduction  
Speaker identification is a prominent research area within the speech recognition domain due to its applications in authentication, forensics, security, and biometrics. It is a subfield of Automatic Speech Recognition (ASR), an interdisciplinary domain encompassing acoustics, signal processing, machine learning, communication, and information theory. ASR can be viewed from two perspectives: speaker identification and speaker verification. The speaker identification aspect addresses the task of identifying a speaker from a group of known individuals. In contrast, speaker verification involves determining whether the suspected person is indeed the correct individual. Both aspects fall under the discipline of acoustics, which involves the study of mechanical waves such as speech sounds [1; 2; 3; 4; 5].  
Each approach has its advantages; speaker identification is crucial for biometric security control, while speaker verification is essential for authenticating personalized interfaces tailored to each speaker. In speaker identification, the features of the input signal are compared with the stored features of all known speakers. In contrast, in speaker verification, the input signal features are compared only to the stored features of the target individual's speech [6; 7; 8; 9; 10].  
However, there are still some significant issues with i-vectors. Moreover, some significant challenges in speaker identification systems still impact their accuracy and overall performance. The first issue is the system's robustness to noise, as noisy environments can significantly degrade identification accuracy. The second issue is the general performance of these systems, particularly in maintaining high accuracy across different conditions [11; 12; 13; 14; 15; 16].  
To address these challenges, we propose an ensemble approach that combines the strengths of RNNs, DNNs, and softmax. This ensemble technique enhances both noise resilience and accuracy. In contrast to previous works, we evaluate the performance of VAD, DNNs, RNNs, and softmax within the context of a speaker identification system. The accuracy of our system is compared with state-of-the-art techniques that primarily utilize Long Short-Term Memory (LSTM), Gaussian Mixture Models (GMM), and i-vector-based approaches. Similar to other studies in areas such as session compensation [1], unsupervised speaker identification [1; 17; 18; 19; 1; 20; 1], speaker/speech separation [20], text-independent identification [21; 22; 23; 24], and natural language parsing [25], we apply our method to a widely recognized dataset and evaluate its effectiveness using well-established performance metrics. The major contributions of our work are as follows:  
* We tackle the critical challenge of noise robustness in speaker identification by proposing a novel ensemble of an RNN and DNN with softmax, which significantly improves the system's accuracy in noisy environments.
* Our method is rigorously tested on a well-known dataset, demonstrating its reliability and effectiveness. The system is evaluated against standard performance measures to ensure valid and comparable results with existing approaches.
* We contribute a novel integration of our approach with existing techniques like session compensation and text-independent identification. This comprehensive solution advances the field by improving accuracy across a variety of conditions and speaker identification tasks.