Approximate Entropy and Empirical Mode Decomposition for Improved Speaker Recognition

<jats:p> When processing real-world recordings of speech, it is highly probable noise will be present at some instance in the signal. Compounding this problem is the situation when the noise occurs in short, impulsive bursts at random intervals. Traditional signal processing methods used to detect speech rely on the spectral energy of the incoming signal to make a determination whether or not a segment of the signal contains speech. However when noise is present, this simple energy detection is prone to falsely flagging noise as speech. This paper will demonstrate an alternative way of processing a noisy speech signal utilizing a combination of information theoretic and signal processing principles to differentiate speech segments from noise. The utilization of this preprocessing technique will allow a speaker recognition system to train statistical speaker model using noise-corrupted speech files, and construct models statistically similar to those constructed from noise-free data. This preprocessing method will be shown to outperform traditional spectrum-based methods for both low-entropy and high-entropy noise in low signal-to-noise ratio environments, with a reduction in the feature space distortion when measured using the Cauchy–Schwarz (CS) distance metric. /jats:p

Electronic version of an article published as 'Advances in Data Science and Adaptive Analysis', 12, 03n04, 2020, 2050011 10.1142/s2424922x20500114 © World Scientific Publishing Company https://doi.org/10.1142/s2424922x20500114

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Work Title Approximate Entropy and Empirical Mode Decomposition for Improved Speaker Recognition
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Open Access
Creators
  1. Richard A. Metzger
  2. John F. Doherty
  3. David M. Jenkins
  4. Donald L. Hall
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. World Scientific Pub Co Pte Lt
Publication Date July 2020
Publisher Identifier (DOI)
  1. 10.1142/s2424922x20500114
Source
  1. Advances in Data Science and Adaptive Analysis
Deposited September 09, 2021

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  • Added Creator John F. Doherty
  • Added Creator David M. Jenkins
  • Added Creator Donald L. Hall
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