Wavelet Scattering Transform and CNN for Closed Set Speaker Identification

Wajdi Ghezaiel (1) , Luc Brun (2) , Olivier Lézoray (2)

1 NormaStic - Fédération Normande de Recherche en Sciences et Technologies de l’Information et de la Communication
2 Equipe Image - Laboratoire GREYC - UMR6072

In real world applications, the performances of speaker identification systems degrade due to the reduction of both the amount and the quality of speech utterance. For that particular purpose, we propose a speaker identification system where short utterances with few training examples are used for person identification. Therefore, only a very small amount of data involving a sentence of 2-4 seconds is used. To achieve this, we propose a novel raw waveform end-to-end convolutional neural network (CNN) for text-independent speaker identification. We use wavelet scattering transform as a fixed initialization of the first layers of a CNN network, and learn the remaining layers in a supervised manner. The conducted experiments show that our hybrid architecture combining wavelet scattering transform and CNN can successfully perform efficient feature extraction for a speaker identification, even with a small number of short duration training samples.

Mots clés: Speaker identification; short utterances; wavelet scattering transform; convolutional neural network; hybrid net- work

Domaines: Traitement des images [eess.IV]

Wajdi Ghezaiel, Luc Brun, Olivier Lézoray. Wavelet Scattering Transform and CNN for Closed Set Speaker Identification. International Workshop on Multimedia Signal Processing (MMSP), Sep 2020, Tampere (Virtual conference), Finland. ⟨hal-02955532⟩