Comparison of Four Approaches to Age and Gender Recognition for Telephone Applications

• Main Authors: Metze, F., Ajmera, J., Englert, R., Bub, U., Burkhardt, F., Stegmann, J., Muller, C., Huber, R., Andrassy, B., Bauer, J. G., Littel, B.
• Format: Conference Proceeding
• Language: English
• Subjects: acoustic signal analysis; age; Application specific integrated circuits; Automatic speech recognition; Bayesian methods; gender; Hidden Markov models; Humans; Laboratories; Linear discriminant analysis; speaker classification; speech processing; Telephony; Testing
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP.2007.367263

This paper presents a comparative study of four different approaches to automatic age and gender classification using seven classes on a telephony speech task and also compares the results with human performance on the same data. The automatic approaches compared are based on (1) a parallel phone recognizer, derived from an automatic language identification system; (2) a system using dynamic Bayesian networks to combine several prosodic features; (3) a system based solely on linear prediction analysis; and (4) Gaussian mixture models based on MFCCs for separate recognition of age and gender. On average, the parallel phone recognizer performs as well as Human listeners do, while loosing performance on short utterances. The system based on prosodic features however shows very little dependence on the length of the utterance.