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Speech Analytics Based on Machine Learning

  • Grazina Korvel
  • Adam Kurowski
  • Bozena KostekEmail author
  • Andrzej Czyzewski
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 149)

Abstract

In this chapter, the process of speech data preparation for machine learning is discussed in detail. Examples of speech analytics methods applied to phonemes and allophones are shown. Further, an approach to automatic phoneme recognition involving optimized parametrization and a classifier belonging to machine learning algorithms is discussed. Feature vectors are built on the basis of descriptors coming from the music information retrieval (MIR) domain. Then, phoneme classification beyond the typically used techniques is extended towards exploring Deep Neural Networks (DNNs). This is done by combining Convolutional Neural Networks (CNNs) with audio data converted to the time-frequency space domain (i.e. spectrograms) and then exported as images. In this way a two-dimensional representation of speech feature space is employed. When preparing the phoneme dataset for CNNs, zero padding and interpolation techniques are used. The obtained results show an improvement in classification accuracy in the case of allophones of the phoneme /l/, when CNNs coupled with spectrogram representation are employed. Contrarily, in the case of vowel classification, the results are better for the approach based on pre-selected features and a conventional machine learning algorithm.

Notes

Acknowledgements

Research partially sponsored by the Polish National Science Centre, Dec. No. 2015/17/B/ST6/01874. This work has also been partially supported by Statutory Funds of Electronics, Telecommunications and Informatics Faculty, Gdansk University of Technology.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Grazina Korvel
    • 1
    • 2
  • Adam Kurowski
    • 1
    • 3
  • Bozena Kostek
    • 4
    Email author
  • Andrzej Czyzewski
    • 1
  1. 1.Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems DepartmentGdańsk University of TechnologyGdańskPoland
  2. 2.Institute of Data Science and Digital TechnologiesVilnius UniversityVilniusLithuania
  3. 3.Faculty of Electronics, Telecommunications and InformaticsGdańsk University of TechnologyGdańskPoland
  4. 4.Faculty of Electronics, Telecommunications and Informatics, Audio Acoustics LaboratoryGdańsk University of TechnologyGdańskPoland

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