An Improved Approach for Text-Independent Speaker Recognition

This paper presents new Speaker Identification
and Speaker Verification systems based on the use of new feature
vectors extracted from the speech signal. The proposed structure
combine between the most successful Mel Frequency Cepstral
Coefficients and new features which are the Short Time Zero
Crossing Rate of the signal. A comparison between speaker
recognition systems based on Gaussian mixture models using the
well known Mel Frequency Cepstral Coefficients and the novel
systems based on the use of a combination between both reduced
Mel Frequency Cepstral Coefficients features vectors and Short
Time Zero Crossing Rate features is given. This comparison
proves that the use of the new reduced feature vectors help to
improve the system’s performance and also help to reduce the
time and memory complexity of the system which is required for
realistic applications that suffer from computational resource
limitation. The experiments were performed on speakers from
TIMIT database for different training durations. The suggested
systems performances are evaluated against the baseline systems.
The increase of the proposed systems performances are well
observed for identification experiments and the decrease of
Equal Error Rates are also remarkable for verification
experiments. Experimental results demonstrate the effectiveness
of the new approach which avoids the use of more complex
algorithms or the combination of different approaches requiring
lengthy calculation