“Text-independent Speaker Recognition System based on FPGA” is a text-independent speaker recognition system i.e. the task of the system is to identify /verify the speaker regardless of what is being spoken. The system is implemented on FPGA, since it makes the system robust and efficient than software implementation in single microprocessor.
Speech features are classified as either low-level or high-level characteristics. High-level speech features are associated with syntax, dialect, and the overall meaning of a spoken message. In contrast, low-level features such as pitch and phonemic spectra are associated much more with the physiology of the human vocal tract. It is these low-level features that are also the easiest and least computationally intensive characteristics of speech to extract.
In the system, for speaker recognition, these features are extracted using Mel-Frequency Cepstral Coefficients (MFCCs) .Once extracted, speaker recognition systems attempt to fit these features best to statistical classification model, Gaussian Mixture Model (GMM). The system implementation is done in two phase: initially, the software implementation of the system is done using MATLAB then finally, the hardware implementation is done in Xilinx Spartan 3E FPGA board. Text-Independent speaker recognition system verifies the identity of the speaker only on the basis of the speaker’s voice characteristic. Therefore, it can be used as important Biometric system for the security purpose as voice is only biometric that allows users to authenticate remotely.
An important application of speaker recognition technology is forensic applications where there is no control over the speakers to access the system. The speaker's voice can be used to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers using the system.