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Shennong: A Python toolbox for audio speech features extraction.

Mathieu Bernard1,2, Maxime Poli3, Julien Karadayi3

  • 1Cognitive Machine Learning, PSL Research University, CNRS, EHESS, ENS, Inria, Paris, France. mathieu.bernard.2@cnrs.fr.

Behavior Research Methods
|February 7, 2023
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Summary
This summary is machine-generated.

Shennong is a new Python toolbox for extracting audio speech features using advanced algorithms. This open-source tool simplifies speech analysis for researchers and developers, integrating with existing machine learning workflows.

Keywords:
Features extractionPitch estimationPythonSoftwareSpeech processing

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Area of Science:

  • Speech Processing
  • Computational Linguistics
  • Machine Learning

Background:

  • Accurate audio speech feature extraction is crucial for various applications, including speech recognition and speaker identification.
  • Existing toolboxes may lack comprehensive algorithm implementations or user-friendly interfaces.

Purpose of the Study:

  • To introduce Shennong, an open-source Python toolbox for audio speech feature extraction.
  • To provide a reliable and extensible framework integrating state-of-the-art algorithms.
  • To demonstrate Shennong's utility through practical applications and benchmarks.

Main Methods:

  • Implementation of diverse algorithms: spectro-temporal filters (e.g., Mel-Frequency Cepstral Filterbank), predictive linear filters, pre-trained neural networks, pitch estimators, speaker normalization, and post-processing.
  • Development as a Python toolbox and command-line utility, built upon the Kaldi speech processing library.
  • Integration with the Python ecosystem for machine learning and speech modeling tools.

Main Results:

  • Shennong offers a wide range of well-established and state-of-the-art algorithms for speech feature extraction.
  • The toolbox is designed for ease of use by non-technical users and seamless integration with other Python tools.
  • Applications demonstrate benchmarking of feature extraction, analysis of speaker normalization performance, and comparison of pitch estimation algorithms under noise.

Conclusions:

  • Shennong provides a valuable, open-source resource for researchers and developers in speech processing.
  • Its comprehensive algorithm set and Python integration facilitate advanced speech analysis and machine learning tasks.
  • The demonstrated applications highlight Shennong's effectiveness in diverse speech-related research scenarios.