End-to-End Speaker Profiling Using 1D CNN Architectures and Filter Bank Initialization

Authors

  • Umniah Hameed Jaid Department of Computer Science, College of Science,University of Baghdad https://orcid.org/0000-0002-2109-4670
  • Alia Karim AbdulHassan Computer Science Department- University of Technology

DOI:

https://doi.org/10.3991/ijoe.v19i10.39061

Keywords:

Age Estimation, Height Estimation, Gender Detection, Gammatone filter bank, Wavelet filter bank

Abstract


The automatic estimation of speaker characteristics, such as height, age, and gender, has various applications in forensics, surveillance, customer service, and many human-robot interaction applications. These applications are often required to produce a response promptly. This work proposes a novel approach to speaker profiling by combining filter bank initializations, such as continuous wavelets and gammatone filter banks, with one-dimensional (1D) convolutional neural networks (CNN) and residual blocks. The proposed end-to-end model goes from the raw waveform to an estimated height, age, and gender of the speaker by learning speaker representation directly from the audio signal without relying on handcrafted and pre-computed acoustic features. The conducted experiments on the TIMIT dataset show that the proposed approach outperforms many previous studies on speaker profiling with a mean absolute error (MAE) of 5.18 and 4.91 cm in height estimation and MAE of 5.36 and 6.07 years in age estimation for males and females, respectively, and achieving an accuracy of 99.98% in gender prediction.

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Published

2023-08-01

How to Cite

Jaid, U. H., & AbdulHassan, A. . (2023). End-to-End Speaker Profiling Using 1D CNN Architectures and Filter Bank Initialization. International Journal of Online and Biomedical Engineering (iJOE), 19(10), pp. 65–81. https://doi.org/10.3991/ijoe.v19i10.39061

Issue

Section

Papers