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Speech Intelligibility Based Enhancement System Using Modified Deep Neural Network and Adaptive Multi-band Spectral Subtraction

  • Tusar Kanti DashEmail author
  • Sandeep Singh Solanki
Article
  • 25 Downloads

Abstract

In contrast to the adverse environments, performances of existing speech enhancement algorithms do not always produce satisfactory results. In the case of worst signal to noise ratio, the processing is complicated and it may introduce signal distortions and degradation of intelligibility. To overcome the complexity of the existing speech enhancement algorithms, a hybrid concept for enhancing the speech quality and intelligibility is proposed in this research. The primary objectives of the research work is to increase the intelligibility of the speech enhancement system that has been trained for a particular speech signal using modified deep neural network (DNN) and adaptive multi-band spectral subtraction (AdMBSS). In this work, AdMBSS is used for enhancing the intelligibility of the speech signal using the additional phase information calculation, and finally, hybrid DNN and Nelder Mead optimization is utilized to improve the signal quality. Experimental results explain that the proposed framework achieves improved performance in signal to noise ratio, perceptual evaluation of signal quality and minimum mean square error. Finally, performances are taken for the more noises like bus noise, train noise, babble noise, airport noise, station noise and exhibition noise.

Keywords

Speech enhancement Speech intelligibility Feature extraction Optimization 

Notes

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electronics and Communication EngineeringBirla Institute of TechnologyMesraIndia
  2. 2.Electronics and Telecom EngineeringCV Raman College of EngineeringBhubaneswarIndia

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