Abstract
This paper assesses the interest of the multiscale Wavelet Scattering Transform (WST) for Speaker identification (SI) applied in several depths and invariance scales. Our primary purpose is to present an approach to optimally design the WST to enhance the identification accuracy for short utterances. We describe the invariant features offered by the depth of this transform by performing simple experiments based on text-independent and text-dependent SI. To compete the state-of-the-art (SOTA), we propose a fusion method between WST and x-vectors architecture, we show that this structure outperforms HWSTCNN by \(7.57\%\) on TIMIT dataset sampled at 8 kHz and makes the same performance in the SOTA at 16 kHz.
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Moufidi, A., Rousseau, D., Rasti, P. (2023). Wavelet Scattering Transform Depth Benefit, An Application for Speaker Identification. In: El Gayar, N., Trentin, E., Ravanelli, M., Abbas, H. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2022. Lecture Notes in Computer Science(), vol 13739. Springer, Cham. https://doi.org/10.1007/978-3-031-20650-4_8
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DOI: https://doi.org/10.1007/978-3-031-20650-4_8
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