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Speech Enhancement for Speaker Recognition Using Deep Recurrent Neural Networks

  • Maxim Tkachenko
  • Alexander Yamshinin
  • Nikolay Lyubimov
  • Mikhail Kotov
  • Marina Nastasenko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10458)

Abstract

This paper describes the speech denoising system based on long short-term memory (LSTM) neural networks. The architecture of the presented network is designed to make speech enhancement in spectrogram magnitude domain. The audio resynthesis is performed via the inverse short-time Fourier transform by maintaining the original phase. Objective quality is assessed by root mean square error between clean and denoised audio signals on CHiME corpus and speaker verification rate by using RSR2015 corpus. Proposed system demonstrates improved results on both metrics.

Keywords

Noise suppression Denoising Speech restoration LSTM Neural networks Speaker verification 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Maxim Tkachenko
    • 1
  • Alexander Yamshinin
    • 1
  • Nikolay Lyubimov
    • 2
  • Mikhail Kotov
    • 1
  • Marina Nastasenko
    • 3
  1. 1.ASM Solutions LLCMoscowRussia
  2. 2.Lomonosov Moscow State UniversityMoscowRussia
  3. 3.Master Synthesis LLCMoscowRussia

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