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International Conference on Machine Learning for Networking

MLN 2020: Machine Learning for Networking pp 245–254Cite as

Spectral Analysis for Automatic Speech Recognition and Enhancement

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12629))

Abstract

Accurate recognition of noisy speech signal is still an obstacle for wider application of speech recognition technology. The robustness of a speech recognition system is heavily influenced by the ability to handle the presence of background noise. In this work, a Short Time Fourier Transform (STFT) filtering technique for the enhancement and recognition of the speech signal is presented. Conventionally, STFT filtering has been applied in speech analysis. However, in this study the combination of modified STFT with Adaptive window width based on the Chirp Rate, termed ASTFT, in conjunction with Spectrogram Features is proposed for optimal speech recognition and enhancement. LibriSpeech ASR Corpus is the benchmark dataset for this experiment. The spectrum from the enhanced Speech signal is estimated using several spectrogram features to obtain a unit peak amplitude. Priori Signal-to-Noise Ratio (SNR) estimation is performed on the modified STFT speech signal, and it achieved an SNR of 31.86 dB which is considered to be an effectively clean speech signal.

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Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, 4000, South Africa

    Jane Oruh & Serestina Viriri

Authors
  1. Jane Oruh
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  2. Serestina Viriri
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Corresponding author

Correspondence to Serestina Viriri .

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Editors and Affiliations

  1. Laboratoire LIGM UMR 8049 CNRS, ESIEE Paris, Noisy-le-Grand, France

    Éric Renault

  2. CNAM/CEDRIC, Paris, France

    Selma Boumerdassi

  3. Inria/EVA Project, Paris, France

    Paul Mühlethaler

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Oruh, J., Viriri, S. (2021). Spectral Analysis for Automatic Speech Recognition and Enhancement. In: Renault, É., Boumerdassi, S., Mühlethaler, P. (eds) Machine Learning for Networking. MLN 2020. Lecture Notes in Computer Science(), vol 12629. Springer, Cham. https://doi.org/10.1007/978-3-030-70866-5_16

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  • DOI: https://doi.org/10.1007/978-3-030-70866-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70865-8

  • Online ISBN: 978-3-030-70866-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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