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The estimation of speaker characteristics such as age and height is achallenging task, having numerous applications in voice forensic analysis. Inthis work, we propose a bi-encoder transformer mixture model for speaker ageand height estimation. Considering the wide differences in male and femalevoice characteristics such as differences in formant and fundamentalfrequencies, we propose the use of two separate transformer encoders for theextraction of specific voice features in the male and female gender, usingwav2vec 2.0 as a common-level feature extractor. This architecture reduces theinterference effects during backpropagation and improves the generalizabilityof the model. We perform our experiments on the TIMIT dataset and significantlyoutperform the current state-of-the-art results on age estimation.Specifically, we achieve root mean squared error (RMSE) of 5.54 years and 6.49years for male and female age estimation, respectively. Further experiment toevaluate the relative importance of different phonetic types for our taskdemonstrate that vowel sounds are the most distinguishing for age estimation.

Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model