Speaker identification using higher order spectral phase features and their effectiveness vis-a-vis Mel-Cepstral features

The effectiveness of higher-order spectral (HOS) phase features in speaker recognition is investigated by comparison with Mel Cepstral features on the same speech data. HOS phase features retain phase information from the Fourier spectrum unlikeMel–frequency Cepstral coefficients (MFCC). Gaussian mixture models are constructed from Mel– Cepstral features and HOS features, respectively, for the same data from various speakers in the Switchboard telephone Speech Corpus. Feature clusters, model parameters and classification performance are analyzed. HOS phase features on their own provide a correct identification rate of about 97% on the chosen subset of the corpus. This is the same level of accuracy as provided by MFCCs. Cluster plots and model parameters are compared to show that HOS phase features can provide complementary information to better discriminate between speakers.

Why do so few hold stocks : theory and evidence

This study develops a life-cycle model where investors make investment decisions in a realistic environment. Model results show that personal illiquid projects (housing and children), fixed costs (once-off/per-period participation costs plus variable/fixed transaction costs) and endogenous risky human capital (with permanent, transitory and disastrous shocks) together are able to address both the non-participation puzzle and the age-effects puzzle. Empirical implications of the model are examined using Heckman’s two-step method with the latest five Surveys of Consumer Finance (SCF). Regression results show that liquidity, informational cost and human capital are indeed the major determinants of participation and asset allocation decisions at different stages of an investor’s life.