Shennong: A Python toolbox for audio speech features extraction

• Published in: Behavior research methods Vol. 55; no. 8; pp. 4489 - 4501
• Main Authors: Bernard, Mathieu, Poli, Maxime, Karadayi, Julien, Dupoux, Emmanuel
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2023; Springer Nature B.V
• Subjects: Algorithms; Behavioral Science and Psychology; Cognitive Psychology; Economics and Finance; Ecosystem; Filters; Frequency; Humanities and Social Sciences; Humans; Neural networks; Neural Networks, Computer; Psychology; Software; Speech; Temporal variations; Pitch estimation; Software; Speech processing; Features extraction; Python; No keyword available
• ISSN: 1554-3528; 1554-351X; 1554-3528
• DOI: 10.3758/s13428-022-02029-6

We introduce Shennong, a Python toolbox and command-line utility for audio speech features extraction. It implements a wide range of well-established state-of-the-art algorithms: spectro-temporal filters such as Mel-Frequency Cepstral Filterbank or Predictive Linear Filters, pre-trained neural networks, pitch estimators, speaker normalization methods, and post-processing algorithms. Shennong is an open source, reliable and extensible framework built on top of the popular Kaldi speech processing library. The Python implementation makes it easy to use by non-technical users and integrates with third-party speech modeling and machine learning tools from the Python ecosystem. This paper describes the Shennong software architecture, its core components, and implemented algorithms. Then, three applications illustrate its use. We first present a benchmark of speech features extraction algorithms available in Shennong on a phone discrimination task. We then analyze the performances of a speaker normalization model as a function of the speech duration used for training. We finally compare pitch estimation algorithms on speech under various noise conditions.