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Deep Learning Based Speech Quality Assessment Focusing on Noise Effects

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Speech and Computer (SPECOM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14339))

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Abstract

This paper investigates the suitability of different speech features in measuring and monitoring speech quality in order to fulfil the expected level of human perceived quality of experience (QoE) while using applications, such as Microsoft Skype, and Apple FaceTime to name a few. To this end, two speech features, namely; line spectral frequencies (LSF), and multi-resolution auditory model (MRAM) are extracted from the speech signal after processing it through a voice activity detector (VAD). A series of deep neural network (DNN)-based objective no-reference speech quality models (SQMs) are then developed employing a single speech feature and combining both speech features. Two noisy speech datasets, namely; Supplement-23 and NOIZEUS-2240 are used for the experiment. Simulation results demonstrate that the SQM developed using combined speech features results in a better speech quality prediction as compared to the SQM developed using a single speech feature, when tested with distinct types of speech degradations.

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Author information

Authors and Affiliations

  1. Department of Information and Communication Technology, University of Agder, Grimstad, Norway

    Rahul Jaiswal

  2. Scaler Neovarsity, Bangalore, India

    Anu Priya

Authors
  1. Rahul Jaiswal
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  2. Anu Priya
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Corresponding authors

Correspondence to Rahul Jaiswal or Anu Priya .

Editor information

Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  2. Koneru Lakshmaiah Education Foundation, Vaddeswaram, India

    K. Samudravijaya

  3. Indian Institute of Information Technology Dharwad, Dharwad, India

    K. T. Deepak

  4. Indian Institute of Technology Dharwad, Dharwad, India

    Rajesh M. Hegde

  5. KIIT Group of Colleges, Gurugram, India

    Shyam S. Agrawal

  6. Indian Institute of Technology Dharwad, Dharwad, India

    S. R. Mahadeva Prasanna

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Jaiswal, R., Priya, A. (2023). Deep Learning Based Speech Quality Assessment Focusing on Noise Effects. In: Karpov, A., Samudravijaya, K., Deepak, K.T., Hegde, R.M., Agrawal, S.S., Prasanna, S.R.M. (eds) Speech and Computer. SPECOM 2023. Lecture Notes in Computer Science(), vol 14339. Springer, Cham. https://doi.org/10.1007/978-3-031-48312-7_22

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  • DOI: https://doi.org/10.1007/978-3-031-48312-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48311-0

  • Online ISBN: 978-3-031-48312-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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