106th Commencement

Video of Clark University's 106th Commencement. Commencement speaker was Alan Khazei.

Perspective: LaGuardia Community College Newsletter Vol. 1, No. 3

"A newsletter published periodically to keep the faculty, students, staff, and community informed about the activities taking place on the campus of LaGuardia Community College." No date [1973]; Vol. 1, No. 3; 4 pages; Color (green, black & white); black & white photographs. Cover article: “COMMENCEMENT SPEAKER NAMED, SHIRLEY CHISHOLM TO DELIVER GRADUATION ADDRESS”. Other entries include: “FIORELLO FUNDS COMMITTEE ANNOUNCES F INAL AWARDS;” “’TOWN MEETINGS’ PROGRAMS TO BEGIN ON TELEVISION MARCH 17;” “Highights of BLACK HISTORY WEEK.”

Speaker at a New York Trade School Commencement Ceremony

An administrator from the New York Trade School speaks at the school's commencement ceremony. In front of the speaker several athletic awards are positioned on a table. Black and white photograph.

Speaker at a New York Trade School Commencement Ceremony

A speaker at the 1957 commencement ceremony at the New York Trade School is shown. George E. McLaughlin, superintendent of the school, can be seen amongst other administrators on the stage behind the speaker. Black and white photograph.

Speaker at a New York Trade School Commencement Ceremony

A speaker at a commencement at the New York Trade School. Other administrators can be seen on the dais behind the speaker. Photograph is black and white.

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.