Speaker recognition system using different feature extraction techniques using autoencoder.

A speaker recognition system is a technology designed to identify and verify the identity of an individual based on their unique voice characteristics. It falls under the broader category of biometric authentication systems, which use various physical and behavioral traits to identify or authenticate individuals. Feature extraction plays a crucial role in a speaker recognition system as it is the process of converting raw speech signals into a compact and representative set of features that can effectively capture the unique characteristics of a person's voice. This step is vital because raw speech signals are complex and high-dimensional, containing a vast amount of redundant and irrelevant information. By extracting relevant features, the system can focus on the essential aspects of the speaker's voice, enhancing accuracy and efficiency. Effective feature extraction is essential for dealing with variations in speech due to different accents, speaking styles, or emotional states. By capturing the distinctive aspects of a person's voice, regardless of these variations, the system can achieve robust and reliable performance. Additionally, feature extraction significantly reduces the computational complexity of the recognition process, making it feasible for real-time applications. In this paper, the speaker recognition system utilizes an autoencoder for modeling purposes. Additionally, the study explores various feature extraction techniques to enhance the system's performance. These techniques include MFCC (Mel-Frequency Cepstral Coefficients), Pitch, Jitter, Wavelet Transform, Wavelet Packet Transform, and Shimmer feature extraction methods. [ABSTRACT FROM AUTHOR]