An MFCC-Based Speaker Identification System
Nowadays, many speech recognition applications have been used by people in the world. Typical examples are the SIRI of iPhone, Google speech recognition system, and mobile phones operated by voice, etc. On the contrary, speaker identification in its current stage is relatively immature. Therefore, in this paper, we study a speaker identification technique which first takes the original voice signals of a person, e.g., Bob, and then normalizes the audio energies of the signals. After that, the audio signals is converted from time domain to frequency domain by employing Fourier transformation approach. Next, a MFCC-based human auditory filtering model is utilized to identify the energy levels of different frequencies as the quantified characteristics of Bob's voice. Further, the probability density function of Gaussian mixture model is utilized to indicate the distribution of the quantified characteristics as Bob's specific acoustic model. When receiving an unknown person, e.g., x's voice, the system processes the voice with the same procedure, and compares the processing result, which is x's acoustic model, with known-people's acoustic models collected in an acoustic-model database beforehand to identify who the most possible speaker is.