Röstigenkänning genom Hidden Markov Model : En implementering av teorin på DSP

Sammanfattning: This master degree project is how to implement a speech recognition system on a DSK – ADSP-BF533 EZ-KIT LITE REV 1.5 based on the theory of the Hidden Markov Model (HMM). The implementation is based on the theory in the master degree project Speech Recognition using Hidden Markov Model by Mikael Nilsson and Marcus Ejnarsson, MEE-01-27. The work accomplished in the project is by reference to the theory, implementing a MFCC, Mel Frequency Cepstrum Coefficient function, a training function, which creates Hidden Markov Models of specific utterances and a testing function, testing utterances on the models created by the training-function. These functions where first created in MatLab. Then the test-function where implemented on the DSK. An evaluation of the implementation is performed.