Comparing MFCC with AR features

Now we compare MFCC feature chains with an AR feature chain using data from both AR and MFCC-based circularly-stationary models. In Figure 11.3, we show the acid test results for data generated according to the circular AR model. The accuracy of the AR feature chain is far superior.

**Figure 11.3:** Acid test performance of AR features (left) and MFCC features (right) for data generated according to the circular AR model.
$\includegraphics[height=3.0in,width=3.0in]{ar_ar_mftest.eps}$ $\includegraphics[height=3.0in,width=3.0in]{mf_ar_mftest.eps}$

In Figure 11.4, we show the acid test results for data generated according to the circular MFCC model. The accuracy of the MFCC feature chain is far superior.

**Figure 11.4:** Acid test performance of AR features (left) and MFCC features (right) for data generated according to the circular AR model.
$\includegraphics[height=3.0in,width=3.0in]{ar_mf_mftest.eps}$ $\includegraphics[height=3.0in,width=3.0in]{mf_mf_mftest.eps}$

These experiments underscore the importantce of matching the feature extraction to the data. Notice also that by using the “wrong" freatures, the result is PDF estimation accuracy. For the mis-matched feature, log-likelihood for the projected PDF is always biased significantly lower, never higher! This is because the true PDF is the likelihood function with the highest average likelihood.