  We introduce Shennong, a Python toolbox and command-line utility for speech
features extraction. It implements a wide range of well-established state of
art algorithms including spectro-temporal filters such as Mel-Frequency
Cepstral Filterbanks or Predictive Linear Filters, pre-trained neural networks,
pitch estimators as well as speaker normalization methods and post-processing
algorithms. Shennong is an open source, easy-to-use, reliable and extensible
framework. The use of Python makes the integration to others speech modeling
and machine learning tools easy. It aims to replace or complement several
heterogeneous software, such as Kaldi or Praat. After describing the Shennong
software architecture, its core components and implemented algorithms, this
paper illustrates its use on three applications: a comparison of speech
features performances on a phones discrimination task, an analysis of a Vocal
Tract Length Normalization model as a function of the speech duration used for
training and a comparison of pitch estimation algorithms under various noise
conditions.
