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Semi-Supervised Learning to Enhance Speech Signal for Mobile Communication

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Abstract

Machine learning algorithm to enhance the complex speech signal for mobile communication is one of the research problems in signal processing. The objective of this research paper is to develop a learning algorithm that improves the quality and intelligibility of voice signals that gets are corrupted by real world noise while they are transmitted through the channel. In this paper, we consider a semi-supervised machine learning algorithm for mobile phones that comes with system software to improve SNR of speech signal which is corrupted by manmade disturbance. Most of the disturbances are non-stationary where the effect of noise is non-uniform for all spectral components. In the projected algorithm training, the system is completed with a set of speech and noise data base. System parameters are derived during training process; these parameters are updated as per the disturbance present in the signal. These parameters are used to remove the noise present in speech signal. The obtained results show a substantial progress in SNR by 5–8% as compared to traditional methods.

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Authors and Affiliations

  1. Department of Electronics and Communication, PES University, Bangalore, India

    Purushotham Ugraiah

  2. Department of Electronics and Communication, RVITM, Bangalore, India

    Chethan Kanakapura Shivabasave Gowda

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  1. Purushotham Ugraiah
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  2. Chethan Kanakapura Shivabasave Gowda
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Correspondence to Purushotham Ugraiah.

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This article is part of the topical collection “Computational Statistics” guest edited by Anish Gupta, Mike Hinchey, Vincenzo Puri, Zeev Zalevsky and Wan Abdul Rahim.

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Ugraiah, P., Shivabasave Gowda, C.K. Semi-Supervised Learning to Enhance Speech Signal for Mobile Communication. SN COMPUT. SCI. 1, 364 (2020). https://doi.org/10.1007/s42979-020-00374-x

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