Abstract
Gender and age estimation based on Gaussian Mixture Models (GMM) is introduced. Telephone recordings from the Czech SpeechDat-East database are used as training and test data set. Mel-Frequency Cepstral Coefficients (MFCC) are extracted from the speech recordings. To estimate the GMMs’ parameters Maximum Likelihood (ML) training is applied. Consequently these estimations are used as the baseline for Maximum Mutual Information (MMI) training. Results achieved when employing both ML and MMI training are presented and discussed.
This work was partly supported by European projects AMIDA (IST-033812) and Caretaker (FP6-027231), by Grant Agency of Czech Republic under project No. 102/05/0278 and by Czech Ministry of Education under project No. MSM0021630528. The hardware used in this work was partially provided by CESNET under projects No. 119/2004, No. 162/2005 and No. 201/2006. Lukáš Burget was supported by Grant Agency of Czech Republic under project No. GP102/06/383.
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Hubeika, V., Szöke, I., Burget, L., Černocký, J. (2007). Maximum Likelihood and Maximum Mutual Information Training in Gender and Age Recognition System. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2007. Lecture Notes in Computer Science(), vol 4629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74628-7_64
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DOI: https://doi.org/10.1007/978-3-540-74628-7_64
Publisher Name: Springer, Berlin, Heidelberg
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