Abstract:
In this thesis, we attempted to build speaker identification system. The
purpose of this thesis is to improve the performance of speaker identification
system based on hidden Markov model classifier. We proposed an
approach in which the coefficients of LPCC or MFCC can be increased, the
codebook size can also be increased, and HMM classifier can be trained
and tested with multiple codebooks.
The implementation of the system is divided into three stages:
feature extraction, vector quantization, and training and classification. In
feature extraction stage, we studied LPCC and MFCC by which the spectral
features of speech signal can be estimated. Then we showed how these
features can be computed. In the vector quantization stage, we generated a
distinct codebook of size 256 clusters for each speaker model. In the stage
of
training
and
classification,
we
studied
the
algorithms
and
implementation of HMM. Then we showed how HMM can be trained to
estimate the model parameters using Baum-Welch algorithm and how can
be tested using forward algorithm.
To evaluate the HMM classifier, we extracted a sample from the
Switchboard dataset. The sample consists of recordings of 40 speakers of
American
English.
Experimental
results
show
that
the
average
identification rate of 97.5% has been obtained. Also the results show that
the identification rate of LPCCs is better than that of MFCCs. We founded
that the system can achieve a stable identification rate with a codebook of
size >=256. We compared LDA and MLP classifiers with HMM classifier.
Results illustrated that the HMM classifier achieves a superior results.
These results conclude that the proposed approach can improve the
performance of speaker identification system.
