  Automatic speaker recognition algorithms typically use pre-defined
filterbanks, such as Mel-Frequency and Gammatone filterbanks, for
characterizing speech audio. However, it has been observed that the features
extracted using these filterbanks are not resilient to diverse audio
degradations. In this work, we propose a deep learning-based technique to
deduce the filterbank design from vast amounts of speech audio. The purpose of
such a filterbank is to extract features robust to non-ideal audio conditions,
such as degraded, short duration, and multi-lingual speech. To this effect, a
1D convolutional neural network is designed to learn a time-domain filterbank
called DeepVOX directly from raw speech audio. Secondly, an adaptive triplet
mining technique is developed to efficiently mine the data samples best suited
to train the filterbank. Thirdly, a detailed ablation study of the DeepVOX
filterbanks reveals the presence of both vocal source and vocal tract
characteristics in the extracted features. Experimental results on VOXCeleb2,
NIST SRE 2008, 2010 and 2018, and Fisher speech datasets demonstrate the
efficacy of the DeepVOX features across a variety of degraded, short duration,
and multi-lingual speech. The DeepVOX features also shown to improve the
performance of existing speaker recognition algorithms, such as the
xVector-PLDA and the iVector-PLDA.
